Dave Roberts
email: dave.roberts@sppowersystems.comp.c.taylor@dur.ac.uk

Philip Taylor

Andrea Michiorri
email: andrea.michiorri@dur.ac.uk

Andrea Michiorri Dynamic Thermal

DYNAMIC THERMAL RATING FOR INCREASING NETWORK CAPACITY AND DELAYING NETWORK REINFORCEMENTS

établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

Increasing energy demand and distributed generation (DG) coupled with stringent commercial and environmental considerations have caused DNOs to seek methods of increasing the utilisation of their existing assets. In the United Kingdom (UK) for example in order to meet the government's environmental targets for 2010, approximately 10GW of additional distributed generation will have to be connected to UK distribution networks [START_REF] Macdonald | Capacity mapping and market scenarios for 2010 and 2020[END_REF]. This increased utilisation must be realised cautiously such that it does not reduce the security of supply to customers. This point is further emphasised when the age of distribution network assets is taken into account. In most western countries distribution networks were built between 1960 and 1980 in a period of economic growth associated with high energy consumption. It has been shown [START_REF] Wijnia | Long term optimization of asset replacement in energy infrastructures[END_REF] that as the ageing process continues increased failure rates should be expected along with increased replacement costs. Furthermore, difficulties are often encountered when attempting to gain permission to build new network infrastructure due to planning problems and environmental objections [START_REF] Fox-Penner | Easing Gridlock on the Grid -Electricity Planning and Siting Compacts[END_REF]. One potential solution or means of deferring these problems is the adoption of active management techniques which have the potential, in certain circumstances, to be both less invasive and more cost effective when compared to network reinforcement options. For example, the active management of generation or load can help to reduce power flow peaks preventing lines from reaching their voltage or thermal limits. Active network management techniques can also be used to increase the power flowing through a section of network such that its utilisation is increased in a safe manner. This increased utilisation can be realised within the normal static ratings but the focus of this work is to show how the adoption of dynamic thermal ratings could increase network utilisation further in a cost effective and safe manner.

Related Work

There are a number of research and development projects looking at the issue of dynamic thermal ratings throughout the world. An example of a DTR application in the transmission network in the region of Madrid is described in [START_REF] Soto | Increasing the capacity of OHLs in the 400 kV Spanish transmission network: real time thermal ratings[END_REF]. In this case, a low number of weather stations are used to estimate wind speed and direction over a wide geographical area; the real time rating of the overhead line (OHL), is then calculated. The Electric Power Research Institute (EPRI) developed a similar system in the late 1990s considering OHLs, underground cables (UGCs) and power transformers (PTRs). In [START_REF] Douglass | Real-time monitoring and dynamic thermal rating of power transmission circuits[END_REF] and [START_REF] Douglass | Field application of a dynamic thermal circuit rating method[END_REF] the system and field test results are reported. It was found that for a complete network, rating increases of up to 15% of the static value were possible. OHLs, UGCs and PTRs are also examined in the DTR system described in [START_REF] Nuijten | Future network planning and grid control[END_REF]. Here component dynamic behaviour is taken into account and the advantages of DTRs for DNOs are described. Finally the advantages of DTR systems for accommodating distributed generation, in particular wind power, within the distribution network are described in [START_REF] Belben | Aeolian uprating: how wind farms can solve their own transmission problems[END_REF] and [START_REF] Helmer | Optimized size of wind power plant transformer and parallel operation[END_REF] for OHLs and PTRs respectively. The work described in this paper differs from the work previously described in that it compares cost, transmission capacity and operational risk of a line with a DTR system and other reinforcement alternatives. The paper is organized in the following way. Firstly a description of the DTR system under development at Durham University (DU) is given. This includes an overview of the thermal model used to rate OHLs. Then a case study network is presented and the results of the study are reported and discussed. Finally conclusions relating to the potential advantages of implementing a DTR system are drawn.

DYNAMIC THERMAL RATING

The dynamic thermal rating (DTR) concept is based on the observation that the first limit met for the current carrying capacity of a circuit is its temperature. This parameter is influenced by the ability of the component to dissipate to the environment the heat produced by the joule effect, and by external conditions such as ambient temperature, or wind speed.

Due to the extreme variability and unpredictability of meteorological conditions, conservative conditions are currently used to determine the most appropriate component static rating. A DTR approach to network and load management consists of estimating or measuring component temperature or real current carrying capacity, in order to allow the power system component utilisation to be safely increased.

Research carried out at DU, aiming to develop active network management techniques involving the use of a DTR system, shows that there is an average exploitable headroom for OHLs, UGCs and PTRs in the region of 250%, 12% and 15% of the static component rating, respectively for typical UK climates. This is in agreement with the findings described in [START_REF] Douglass | Real-time monitoring and dynamic thermal rating of power transmission circuits[END_REF]. The DTR system under development at DU comprises a number of different weather stations and temperature measurement devices, placed in different locations over a wide network area. They are connected with a central computer responsible for calculating real time rating estimation. This information may then be used as a decision support tool for the secure operation of the distribution network. This computational device can be placed either in a remote substation or in a centralised control room.

Component rating

This paper focuses on the application of a DTR system for OHLs, but it has also been applied to UGCs and PTRs. The fundamental concept behind the component rating is that the temperature limits of the power component must not be exceeded in order to avoid damaging the component. For OHLs in particular, a temperature rise leads to a reduction in conductor tension and to an increase in the sag. Typical values for maximum conductor temperature are between 50 ºC and 90ºC. Component temperature, is not a constant value but depends upon the energy balance between the heat produced inside the component and the heat exchange on its surface. The energy dissipated depends on the load, however the heat exchange is mainly influenced by the temperature difference between ambient and the environment and by other external factors such as wind speed or solar radiation. Considering the heat dissipated by the Joule effect (RI 2 ), the heat exchanged by convection (Q c ) and radiation (Q r ), and the solar radiation (Q s ), the energy balance for an OHL conductor is described in Equation ( 1).

(1) 21 ,

s c r RI Q Q Q Wm      
The heat gained by solar radiation can be calculated as in Equation ( 2) considering solar radiation (W s ), conductor diameter (D) and an absorption coefficient () (2)

ss Q W D  
The radiative heat exchange depends on conductor temperature (T c ), ambient temperature (T a ), the Stefan-Boltzman constant () and an emission coefficient (), as reported in Equation ( 3).

(

 

The Nusselt number can be calculated using wind direction correction factor (K dir ) and the Reynolds number (Re) as in Equation ( 5).

( The wind direction correction factor and the Reynolds number can in turn be calculated as in Equations ( 6) and ( 7) , using wind direction (W d ), wind speed (W s ) and empirical parameters (A, B, C) More information about the model described can be found in [START_REF] Iec | IEC/TR 61597 -Overhead electrical conductorscalculation methods for stranded bare conductors[END_REF] and [START_REF]The Thermal Behaviour of Overhead Conductors -Section 1[END_REF].

(6)   sin C dir d K A B W 
In Figure 1, the rating of a LYNX conductor exposed to different wind speeds is given. Using the model previously described, an air temperature of 20ºC, a conductor operating temperature of 50ºC and a flow normal to the conductor axis are assumed.

Methodology

In the paragraph below preliminary results from research at DU are reported. Hourly weather data from Valley (Wales, UK) have been used to estimate weather parameter values in each point of the line. These, in conjunction with the model described previously, have been used to calculate a series of real time thermal ratings for the studied line. The method reported in [START_REF] Iec | IEC/TR 61597 -Overhead electrical conductorscalculation methods for stranded bare conductors[END_REF] has been employed for the OHL model, using the correction for wind direction reported in [START_REF]The Thermal Behaviour of Overhead Conductors -Section 1[END_REF]. The real transmission capacity in MVA was then calculated and compared with the static rating currently in use, as well as other more conventional alternatives, such as retensioning the line or reinforcing the network.

Estimates of the costs of these alternatives are also given: re-tensioning costs are calculated from [15] and information from the planning department of SPEN was used to estimate new line construction costs. DTR system costs are estimated by the author.

In order to gain some understanding of the risk associated with the employment of a DTR system, hourly dynamic thermal rating simulation results where compared with hourly simulated load data. This was based on typical data at the site throughout 2005.

Results and discussion

In Figure 3, the real time dynamic ratings are reported and compared with: 1) the static rating attributed to the same line, 2) the static rating of the line re-tensioned for operation up to 75ºC 3) the static rating of the new line for reinforcement with a "Upas" 300mm 2 AAAC conductor at an operational temperature of 75ºC It is possible to see the great variability of real time rating across the year, the low value of the conservative static rating adopted compared to the real time rating and also the risk taken into account when the static rating is set. This underlines the value of a DTR system in preventing thermal overloads.

Figure 3: Real Time and static ratings comparison.

In Table 1 the results shown in Figure 3 are analyzed further, reporting the yearly energy transfer capacity in GWh for the different four different cases. This data is compared with the rating and the cost of each alternative solution. Ratings are taken from [START_REF]Distribution Long Term Development Statement for SP Manweb plc for the years[END_REF] and costs are based on the most appropriate data available at the time of consideration.

Rating In Table 2 the amount of time, in hours and the percentage of time when the real time rating is lower than the static rating, the rating of the re-tensioned line or the reinforced line rating is reported.

[h] [%] Real Time < Static 7 0.08% Real Time < Re-tensioning 75 0.98% Real Time < New line 2855 32.59% Table 2: Time when real time rating is lower than static rating or alternative options. The annual percentage time when the real time rating is lower than the static rating of the reinforced line may appear to be significant. However, the real time dynamic rating must be correlated with load variability to make an accurate assessment of the line utilization. In Figure 4, the numbers of hours the load or the real time rating have a particular value are reported. By analysing these two parameters it is possible to assess the number of hours of potential overload. By considering the probability of a particular circuit load coinciding with a rating smaller than the circuit load, it is possible to calculate the probability of incurring a thermal overload. In the example considered this event would take place for 56 hours per year. This is in agreement with further research at DU which identifies the maximum feasible uprating capacity of a Lynx conductor with a DTR system in the range of 150-180 MVA, according to climatic conditions. When this limit is passed, the number of thermal overload hours rises to unacceptable values.

Figure 4: Load and rating frequencies over the year in hours

A more in depth analysis must take into account the accuracy of the DTR system. Ongoing research at DU aims to assess the precision of a DTR tool with theoretical and field measurement work. At the moment, without experimental results, it is not possible to provide an estimate for the precision of the DTR developed. This precision would depend in particular upon wind speed and wind direction estimation. Whilst errors in wind direction estimation would not affect the results for the whole year, imprecision in wind speed estimates could affect the findings of the work, by providing inaccurate line capability estimation.

CONCLUSION

Advantages and limitations arising from the implementation of a DTR system have been identified by comparing this approach to alternative re-tensioning or network reinforcement solutions. This serves to underline the potential benefits and drawbacks of a DTR approach. From a comparison of installation costs and energy transfer capacity, a DTR system may be able to offer the greatest potential benefits with the lowest cost. In comparison with line re-tensioning, the adoption of a DTR system can provide a 67% gain in energy transfer capacity at 62% of the re-tensioning cost. Moreover, by calculating the hours of potential thermal overload, a DTR system may allow a line to transfer power up to twice that the value of its static rating. It can be concluded that the use of a DTR system has the potential to assist with the accommodation of larger power flows in the existing infrastructure, whilst at the same time increasing operational security.

Finally

  the convective heat exchange depends on air thermal conductivity () and the Nusselt number (Nu)

Figure 1 :

 1 Figure 1: Rating variation of LYNX OHL with wind speed

Figure 2 :

 2 Figure 2: Case study network schematic diagram This represents a typical situation where the possible installation of a wind farm or of an additional load is not influenced only by the wind resource or other environmental and legal issues, but also by the ability of the local network to absorb and/or export the energy produced.

Table 1 : Rating, maximum transmission capacity and cost.

 1

			Capability
		[A]	[GWh/year]	Cost [M£]
	Static rating 390	762	0
	Re-tensioned 520	1034	0.16
	DTR	Variable 1696	0.1
	New line	770	1542	2

ACKNOWLEDGMENTS

The authors wish to acknowledge the Department for Innovation, Universities and Skills for funding, and the staff from ScottishPower, AREVA T&D, Imass, Parsons Brinckerhoff and the Met Office for the help provided.


Samuel Jupe

Philip Taylor

Andrea Michiorri

Christopher Berry

An Evaluation of Distributed Generation Constrained Connection Managers

Keywords: Distributed Generation, Dynamic Thermal Rating, Control, Constrained Connection, Economics

This paper presents the technical considerations and economics of a number of solutions that would allow a greater installed capacity of distributed generation to be connected to, and managed within, the distribution network. The paper describes the various solutions and compares their relative energy yield and economics. It was found that a distributed generation constrained connection manager informed by dynamic thermal ratings is the most attractive solution for developers wishing to connect wind generation to this case study network when compared to alternative solutions.

I. INTRODUCTION

In order to meet the United Kingdom (UK) government"s environmental targets for 2010, approximately 10GW of additional distributed generation (DG) will have to be connected to UK distribution networks [START_REF] Macdonald | The Carbon Trust & DTI Renewables Network Impact Study, Annex 1: Capacity mapping and market scenarios for 2010 and 2020[END_REF]. As this growth in DG is realised, power flows within distribution networks are expected to become increasingly congested. This may act as a barrier, inhibiting the amount of DG that can connect to the network and may impact on the energy yield (and hence profitability) of existing schemes as network capacity becomes saturated. Thus methods to manage the connection of DG schemes, in light of power flow constraints, could be beneficial both in the UK and internationally.

The current practices regarding DG connections may:

1) Constrain the size of schemes at the planning stage to the existing capacity of the network based on static component thermal ratings. 2) Entail relatively high network reinforcement costs to connect schemes in excess of the network"s current capacity. 3) Involve basic "tripping" schemes to manage the DG output at times of constraint.

For the case study considered, comprising DG with an intermittent power output, this paper demonstrates that the energy yield from the unconstrained reinforcement solution is only slightly more than that delivered by an alternative operational solution utilising dynamic circuit ratings. DG constrained connection managers (CCMs) are one approach that facilitates a greater energy yield from DG schemes based on the available network capacity. It is acknowledged that asset ratings are not a static phenomenon but vary as a result of the prevailing meteorological conditions throughout the year. Thus increased amounts of electricity can be passed through distribution networks if the thermal limits of the network components are calculated from present conditions rather than from a fixed seasonal figure.

For the purpose of this research, dynamic thermal ratings (DTRs) are defined as a time-variant rating which can be practically exploited without damaging components or reducing their lifetime. Actual environmental parameter measurements are used as the input to steady state thermal models. In order to calculate and exploit the DTR, it is assumed that local environmental parameters are available and that there are no outages (planned or unplanned) present within the electrical power system. Short term transients, taking into account the thermal capacitance of power system assets are not included within the DTR assessment. It is felt that this would not affect the MWh/annum throughput of energy within the electrical power system.

The 132kV section of the network presented in this paper is a subsection of a wider trial network forming the research basis for the "Active Control of Distributed Generators based on Component Thermal Properties" [START_REF] Neumann | Dynamic thermal rating and active control for improved distribution network utilisation[END_REF]. The collaborative project (involving AREVA T&D, Durham University, Imass, PB Power and ScottishPower EnergyNetworks) aims to develop, install and test a CCM for DG informed by DTRs.

This paper makes a comparison between a network reinforcement solution, two DG tripping solutions and two CCM solutions. Each solution would allow a greater installed capacity of DG to be connected to a single point within the distribution network. The CCM solutions increase in sophistication both in the manner in which the DG power output is controlled (demand-following as opposed to tripping) and by utilising different component rating regimes. By incorporating a backup trip protection system into the more sophisticated solutions, the risk of CCM system failure is minimised and thus the security of the network is maintained. An energy yield, electrical loss and economic evaluation show that, in this case, a CCM informed by DTRs is the most attractive solution for facilitating DG developer revenue gains when compared to the alternative solutions.

II. BACKGROUND

Dynamic Thermal Ratings

Conductor temperature rise is influenced by the ability of the component to dissipate to the environment the heat produced by the Joule effect governed by external conditions such as ambient temperature and wind speed.

Due to the extreme variability and unpredictability of meteorological conditions, fixed seasonal conditions are used at present to determine the most appropriate component rating.

A DTR approach to network management consists of estimating or measuring component temperatures and real current carrying capacities, in order to allow the utilisation of power system components to be safely increased but maintained within continuous design specifications.

Research carried out at Durham University aiming to develop a DTR system shows, for typical UK climates, that there is an average exploitable headroom for overhead lines, underground cables and power transformers in the region of 80%, 12% and 15%, respectively, beyond the static component rating. This is in agreement with the findings described in [START_REF] Belben | Aeolian Uprating: How Wind Farms Can Solve Their Own Transmission Problems[END_REF] and [START_REF] Douglass | Real-time monitoring and dynamic thermal rating of power transmission circuits[END_REF].

The DTR system under development at Durham University comprises a number of different weather stations and temperature measurement devices, placed in different locations over a wide network area. These are connected to a centralised computer which is responsible for estimating the current carrying capacities of the network component based on the present meteorological conditions. This information may then be used as a decision support tool for the secure operation of the distribution network.

DG Constrained Connection Managers

Engineering Technical Recommendation (ETR) 124 [START_REF]Engineering Technical Recommendation 124[END_REF] presents a number of different solutions that may be developed to actively manage the power flows associated with the connection of a single DG scheme. The most basic systems involve the disconnection of DG in the event that the power output from the DG scheme exceeds the capability of the network (assessed as static capacity of network assets plus the minimum load demand). This solution may be developed further by actively switching between seasonal fixed ratings and adjusting the number of disconnected generators accordingly. More sophisticated CCM solutions are developed from the principle of generation power output control, utilising technologies such as the pitch control of wind turbine blades to capture a desired amount of wind energy. In this approach the powers flowing in the critical feeders of the network are monitored, taking load demand into account, and the power exported from the DG scheme is controlled to ensure the capability of the network is not exceeded. This may be developed as a CCM utilising static asset ratings, with demand-following control of the DG output, or as a CCM utilising asset dynamic thermal ratings, with demandfollowing control of the DG output.

III. OVERHEAD LINE DYNAMIC RATING

This paper focuses on the application of a DTR system to overhead lines, but it has also been applied to cables and power transformers. The fundamental concept behind the component rating is that the temperature limits of the power component must not be exceeded in order to avoid damaging the component. For overhead lines in particular, a temperature rise leads to a reduction in conductor tension and to an increase in the sag. Typical values for maximum conductor temperature are between 50ºC and 90ºC.

Component temperature is not a constant value but depends upon the energy balance between the heat produced inside the component and the heat exchange on its surface. The heat dissipated depends on the electrical current flowing in the conductor as well as the conductor"s resistance to the current flow. However, the heat exchange is mainly influenced by the temperature difference between the conductor and ambient environmental conditions, and by other external factors such as wind speed or solar radiation.

Considering the heat dissipated by the Joule effect (I 2 R), the heat exchanged by convection (Q c ) and radiation (Q r ), and the solar radiation (Q s ), the energy balance for an overhead line conductor is described in Equation (1). ,

s c r RI Q Q Q Wm         (1)
The heat gained by solar radiation can be calculated as in Equation ( 2) considering solar radiation (W s ), conductor diameter (D) and an absorption coefficient (α).

s s Q W D   (2)
The radiative heat exchange depends on conductor temperature (T c ), ambient temperature (T a ), the Stefan-Boltzman constant (σ) and an emission coefficient (ε), as given in Equation (3).

 

4 4 r c a Q T T D     (3)
The convective heat exchange depends on air thermal conductivity (λ) and the Nusselt number (Nu) as given in Equation ( 4).

  c c a Q Nu T T    (4)
The Nusselt number can be calculated using a wind direction correction factor (K dir ) and the Reynolds number (Re) as given in Equation [START_REF]Engineering Technical Recommendation 124[END_REF].

  0.2 0.61 0.65 Re 0.23 Re dir Nu K      (5)
The wind direction correction factor and the Reynolds number can, in turn, be calculated as given in Equations ( 6) and (7), using wind direction (W d ), wind speed (W s ) and empirical parameters (A, B, C).

  sin

C dir d K A B W  (6) 1.78 9 Re 1.644 10 2 c a s TT WD             (7)
More information about the overhead line thermal model may be found in [START_REF] Iec | IEC/TR 61597 -Overhead electrical conductorscalculation methods for stranded bare conductors[END_REF] and [START_REF]The Thermal Behaviour of Overhead Conductors -Section 1[END_REF]. The simulated daily dynamic thermal rating of a Lynx conductor is given in Figure 1 for the calendar year 2005. The simulation used the model described in Equations ( 1) to [START_REF]The Thermal Behaviour of Overhead Conductors -Section 1[END_REF] and historical meteorological data for the "Valley" area of Wales, UK. As a comparison, the seasonal ratings for the conductor are also plotted in Figure 1.

IV. CASE STUDY NETWORK

The case study network shown in Figure 2 is derived from a section of ScottishPower"s distribution network. Although it is not displayed in Figure 2, Engineering Recommendation P2/6 [START_REF]Engineering Recommendation P2/6 -Security of Supply[END_REF] "security of supply" requirements are met for the connected load through an underlying meshed 33kV infrastructure. An installed wind capacity of 150MW was selected to create a constrained connection. The constrained connection configurations were simulated through an offline analysis of the typical halfhourly regional loading and wind farm output data for the calendar year 2005.

Table 1 displays the ratings used in the energy yield quantification analyses. The static and seasonal ratings were based on the SP Manweb Long Term Development Statement [START_REF]Scottish Power Transmission and Distribution[END_REF]. The average simulated daily minimum DTR is also given in Table 1. Offline analysis showed that a thermal constraint would be met in this section of the network before voltage or fault-level limitations. Each section below assesses the strengths and weaknesses of the particular solution, describes the algorithm used to manage the DG output and gives an approximate cost of the solution installation.

Tripping Solutions

The tripping solution schematic is shown in Figure 3 and implements the algorithm given in Equation ( 8).

If:

Current > Rating (8) Then: "Trip" DG to Rating + Base Load When this algorithm is implemented with the static rating of 390A, the DG output will be tripped to 434A at unity power factor (390A rating + 44A base load) if the current flow in the line exceeds 390A. This corresponds to the implementation of Solution 1.

Similarly, in a seasonal rating implementation [START_REF] Roberts | Network Management Systems for Active Distribution Networks -A Feasibility Study[END_REF], such as Solution 2, the DG output will be tripped to the seasonal rating plus the base load if line flow exceeds the seasonal rating. These solutions are conservative as they do not account for the dynamic nature of the load and thus they trip generators off rather than constraining them back. Furthermore, the seasonal rating approach bears the latent risk of an anomalous "hot day" where the prevailing meteorological conditions mean that assets may be rated higher than they should be.

Estimated Basic Tripping Relay Cost: Local tripping relay £10k

Figure 3 -The DG trip solution with static or seasonal thermal ratings

Constrained Connection Management Solutions

Figure 4 shows the schematic that allows the algorithm in Equation ( 9) to be implemented to control the DG output based on static or dynamic network availabilities and load demand.

If:

Current > Rating (9) Then: Control DG output to Rating + Load Demand Control algorithm ( 9), implemented with a static rating of 390A, corresponds to Solution 3. The CCM solutions are more sophisticated than the DG trip options and have the potential to offer energy yield gains by taking into account the dynamic nature of the load demand. Additional power flow monitoring equipment is required to facilitate a demand-following DG output control regime.

In the case of the DTR-informed system (Solution 4), additional thermal and meteorological monitoring is also required. To ensure the safe and secure operation of the network assets, each CCM solution requires an auxiliary trip system, which calculates the same ratings as the control system, to act as a backup in the case of CCM system operation failure.

Estimated cost of demand-following DG output control based on static/seasonal ratings:

Network Reinforcement Solution

The network reinforcement option (Solution 5) would require a replacement 132kV overhead line to be constructed and the existing overhead line to be decommissioned. It is assumed that the replacement line conductor is "Upas" 300mm 2 AAAC. If this conductor is tensioned to maintain statutory ground clearances [START_REF] Dti | The Electricity Safety, Quality and Continuity Regulations[END_REF] at an operational temperature of 75 o C, the rating would be sufficient to provide an unconstrained annual energy yield from the DG scheme. However, it requires the largest capital investment [START_REF] Mcdonald | The Carbon Trust and DTI Renewables Network Impact Study, Annex 3: Distribution Network Topography Analysis[END_REF] and could take several years to be installed due to the lengthy environmental assessments, planning permission, commissioning and building processes.

Estimated Reinforcement Cost:

Installation of up-rated 132kV line (7km) £2M

VI. QUANTIFICATION METHODOLOGY Weather data from Valley (Wales, UK) was used to estimate weather parameter values along the length of the overhead line. These, in conjunction with the model described previously, were used to calculate a series of daily thermal ratings for the studied line. The method detailed in [START_REF] Iec | IEC/TR 61597 -Overhead electrical conductorscalculation methods for stranded bare conductors[END_REF] was used for the overhead line modelling, with the correction for wind direction, as given in [START_REF]The Thermal Behaviour of Overhead Conductors -Section 1[END_REF].

Control algorithms ( 8) and ( 9) were applied to the case study network (with the relevant rating operating regime) and the necessary constraints were implemented offline. The annual energy yield at the DG connection busbar was calculated for each solution, by integrating the real power output of the DG scheme across the year in 30 minute intervals. The per unit electrical losses (I 2 R) resulting from each solution implementation were calculated using the current flowing in the overhead line with per unit resistances of 0.0070 and 0.0041 for the "Lynx" and "Upas" conductors respectively. These were then summated across the year on a half-hourly basis to produce annual energy loss figures. For each solution the net annual revenue was calculated by multiplying the annual energy yield at the DG connection bus by £101.43/MWh (£52.15/MWh wholesale electricity price [START_REF]Quarterly Review of European Gas and Electricity Prices[END_REF] + £49.28/MWh "Renewables Obligation Certificate" sale price [START_REF] Ofgem | Renewables Obligation: Annual Report 2006-2007[END_REF]) and making an adjustment for the cost of the losses incurred by transferring this energy to the slack busbar (calculated as the annual energy losses multiplied by the wholesale electricity price).

The basic tripping scheme based on summer static ratings (Solution 1) was taken as the datum solution with a capital cost of £10k and net annual revenue of £42.35M (based an energy yield at the DG connection busbar of 418.1GWh and 1.3 MWh lost through power transfer to the slack bus). The estimated marginal costs (due to additional network costs), predicted marginal revenues (due to additional energy yield) and marginal losses (resulting from electrical power transfer to the slack busbar and changes in electrical resistance of the line) were compared to this solution. This allowed a basic Net Present Value (NPV) comparison of the alternative solutions, based on their relative marginal costs and marginal revenues. A 10% discount rate and 20 year economic life was assumed [START_REF] Berr | Strategic Environmental Assessment, Wind Background -Cost of Wind Power Generation[END_REF]. The capital cost of the wind farm itself was neglected as this would be constant across each solution. Furthermore, because the wind farm is connected at via a single overhead line, any faults or scheduled maintenance on this line will cause it to shut down. Since such events have an equal constraint on the energy yield of each solution this effect was neglected. All the costs within the financial evaluations are estimates of equipment costs, based on the most appropriate data available at the time of consideration.

VII. RESULTS

The results from the quantification methodology are summarised in Table 2. For this case study, it appears that controlling DG output to follow load demand based on a single summer static rating (Solution 3) yields greater revenue for the developer than switching ratings on a seasonal basis and tripping DG as a result (Solution 2). DG tripping based on seasonal thermal ratings (Solution 2) requires a lower initial investment, however, the risk on the part of the DNO is greater if seasonal ratings are utilised. This is to due the possibility of an anomalous hot day occurring when ratings have been relaxed. This risk may be mitigated by investment in a dynamic thermal ratings system to provide accurate knowledge of the current thermal status of the network.

Economically, the most attractive solution to the developer is the CCM based on component dynamic thermal ratings and load demand (Solution 4). The annual revenue of the project is increased by £4.53M and shows the highest marginal NPV at £38.46M. For this case study, this solution appears to be more attractive than the alternative reinforcement option (Solution 5). This provides and unconstrained energy yield (and hence maximum annual revenue) but would require an extra capital investment of £1.99M to upgrade the overhead line.

Network reinforcement (Solution 5) would reduce network losses relative to the other solutions since the larger cross-sectional area of the conductor would reduce the electrical resistance to power flow. However, despite increasing electrical losses through implementing a CCM solution, the cost of capital for the DG developer is likely to make the active management solutions, with lower upfront costs, a more attractive investment.

IX. CONCLUSION This paper has presented the technical solutions that would allow a greater installed capacity of distributed generation to be connected to, and managed within, the distribution network. This could be of value in situations where power flows have become congested as a result of distributed generation proliferation. For each solution the annual energy yield was quantified and used as a basis to compare solutions using an estimate of their relative Net Present Value to the distributed generation developer. It was demonstrated that a constrained connection manager informed by dynamic thermal ratings was the most cost effective solution for facilitating wind generation access to the case study network when compared to alternative

Figure 1 -

 1 Figure 1-Rating variation of Lynx overhead line

  CapacityInjecting power at bus at unity power factor

Figure 2 -

 2 Figure 2 -The 132kV network Analytical Considerations

Figure 4 -

 4 Figure 4 -The demand-following DG output control solution with static or dynamic thermal ratings

Solution 1 :

 1 DG tripping based on a static assessment of network availability Solution 2: DG tripping based on component seasonal thermal ratings Solution 3: DG output control through a CCM based on component static thermal ratings and load demand Solution 4: DG output control through a CCM based on component dynamic thermal ratings and load demand Solution 5: Network reinforcement to provide an unconstrained connection

Table 1 -Summary of Ratings Utilised

 1

	Conductor	Rating	Rating	Rating
	Type	Condition	(A)	(MVA)
	Lynx	Static	390	89
	Lynx	Seasonal Summer	390	89
		Continuous
	Lynx	Seasonal Spring /	450	103
		Autumn Continuous
	Lynx	Seasonal Winter	485	111
		Continuous
	Lynx	DTR (Average Daily	695	159
		Minimum )
	Upas	Static	770	176
	V. SOLUTION DESCRIPTIONS
	Solution 1: DG tripping based on a static assessment of
	network availability
	Solution 2: DG tripping based on component seasonal
	thermal ratings
	Solution 3: DG output control through a CCM based on
	component static thermal ratings and load demand
	Solution 4: DG output control through a CCM based on
	component dynamic thermal ratings and load demand
	Solution 5: Network reinforcement to provide an
	unconstrained connection.

Table 2 -Quantification Methodology Results Solution Marginal Cost (£k) Marginal Annual Energy Yield at DG Connection Bus (%) Marginal Annual Energy Losses (%) Marginal Net Annual Revenue (£M) Marginal 20 Year NPV @ 10% dcf (£M)

 2

	1	0	0.00	0.00	0.00	0.00
	2	0	4.93	18.41	2.08	17.71
	3	40	5.24	18.99	2.21	18.76
	4	90	10.75	43.39	4.53	38.46
	5	1990	10.76	-16.31	4.58	36.97
			VIII. DISCUSSION

solutions. Work is continuing in this area to realise the potential of constrained connection manager solutions.

X. ACKNOWLEDGEMENTS

This work was funded by the Department for Innovation, Universities and Skills (DIUS), the United Kingdom Engineering and Physical Sciences Research Council (EPSRC) and the School of Engineering at Durham University, United Kingdom.

XI. REFERENCES

XII. BIOGRAPHIES

Dr Philip Taylor, BEng, EngD, CEng, MIEE, MIEEE, FHEA Phil Taylor is a Senior Lecturer in the Energy Group in the School of Engineering, Durham University. He received an Engineering Doctorate in the integration and control of renewable energy in electrical networks from the University of Manchester Institute of Science and Technology (UMIST) in 2001.

He has significant industrial experience as an electrical engineer including a period working in the transmission and distribution projects team at GEC Alsthom. His most recent position was Research and Development Director at Econnect, a consultancy firm specialising in the grid integration of renewable energy.

He acts as an independent assessor of research proposals for the DTI relating to their New and Renewable Energy Programme and is a member of the EPSRC Peer Review College.

He is a member of the CIGRE working group C6.11, "Development and Operation of Active Distribution Networks".

He is a member of the editorial board for the newly formed International Journal of Renewable Energy Technology (IJRET) www.inderscience.com/ijret Samuel Jupe, M.Eng (Hons) Dunelm Samuel Jupe is a doctoral researcher in the Energy Group at the School of Engineering, Durham University.

He is currently researching the active control of distributed generators based on component thermal properties. He received a Master of Engineering degree in General Engineering from Durham University in 2006. His interests include the management of distributed generation constrained connections and fuzzy control systems.

Andrea Michiorri

Andrea Michiorri graduated in Mechanical Engineering at the University of Rome "La Sapienza" in 2005. He is a doctoral researcher in the Energy Group at the School of Engineering, Durham University. His research involves the influence of environmental parameters on distribution network components and state estimation techniques. His scientific interests include energy conversion and transmission systems and computer applications in energy.

Christopher Berry, BSc (Hons), CEng, MBA, MIET Christopher Berry has over 25 years experience in the UK electricity industry. He started his career as a Craft Apprentice with British Nuclear Fuels and progressed with them to achieve Chartered Engineer status with responsibility for the design of instrumentation, control and electrical aspects of a number of new major facilities for the Sellafield site. He then joined ScottishPower/Manweb where he has held a variety of power distribution system design and project management positions. Currently he is responsible for design standards for the SP Manweb plc distribution system at all voltage levels and leads a team of professional engineers responsible for the design and development of the 132kV and 33kV networks.

Chris is also an active member of the design work stream of the Distribution Working Group (DWG)the United Kingdom Electricity Supply Industry focus group charged with identifying and co-ordinating R&D work to address the issues that affect the transition of power systems to a low-carbon future. He received his BSc (Hons) in Electrical and Electronic Engineering from the University of Manchester Institute of Science and Technology (UMIST) in 1985 and his MBA (Distinction) from the Edinburgh Business School in 2002. He is a corporate member of the Institution of Engineering and Technology (MIET).


S C E Jupe

P C Taylor

A Michiorri
email: andrea.michiorri@durham.ac.uk

Coordinated output control of multiple distributed generation schemes

Keywords:

This paper presents candidate strategies for the coordinated output control of multiple distributed generation schemes. The proposed strategies are underpinned by power flow sensitivity factors and allow real-time knowledge of power system thermal ratings to be utilised. This could be of value in situations where distribution network power flows require management as a result of distributed generation proliferation. Through off-line open-loop simulations, using historical data from a section of the UK distribution network, the candidate strategies are evaluated against a benchmark control solution in terms of annual energy yields, component losses and voltages. Furthermore, the individual generator annual energy yields and generatorapportioned losses are used to assess the net present values of candidate control strategies to distributed generation developers.

Nomenclature

C 1,2,3 Variable costs (£M) C control Cost of the distributed generator output control system (£M) C install Total wind farm installation costs (£M/MW) C inv Total investment cost for each distributed generation developer (£M) C loss Cost of losses (£M) C OM Cost of distributed generation annual operations and maintenance (£M) C real-time Cost of real-time thermal rating system (£M) C ROC Sale price of Renewables Obligation Certificates (£/MWh) C wholesale Wholesale electricity price (£/MWh) E a Metered annual energy yield of a distributed generator (MWh) E loss Generator-apportioned annual energy loss (MWh) G i Installed capacity of the wind farm (MW) G id Unique identifier (id) of distributed generator, G 0,x,y G P Real power output of the generator at the initial (0), intermediate (x) and final (y) time-steps (MW) G P,m Real power output of distributed generator, G, at node m (MW) 'G P,m Real power output of distributed generator, G, at node m before control actions have been implemented (MW) "G P,m Real power output of distributed generator, G, at node m after control actions have been implemented (MW) ΣG P,m Total real power injection at node m from multiple distributed generators (MW) J Jacobian matrix of AC load flow K Proportionality factor M LIFO Matrix denoting the last-in first-off constraint order of distributed generation schemes M PFSF Matrix of power flow sensitivity factors CAISO Californian independent system operator DG Distributed generation DNO Distribution network operator GSP Grid supply point LIFO Last-in first-off PFSF Power flow sensitivity factor (defined as dP i,k / dG P,m ) ROC

M TMA

Matrix denoting the technically most appropriate constraint order of distributed generation schemes N Number of stake-holder investors in control system

N-1

First circuit outage (electrical contingency)

NPV

Net present value (£M)

P

Vector of real powers (MW) The impetus of governments, on an international scale, to move towards low-carbon economy targets has brought about the proliferation of distributed electricity generation [START_REF] Strbac | Electric power systems research on dispersed generation[END_REF]. However, as the capacity and number of distributed generation (DG) schemes grows, high levels of DG may lead to localised power flow issues within existing distribution networks. Therefore, a requirement is emerging for strategies to control DG power outputs to manage network power flows.

Last-in first-off (LIFO) control strategies for multiple DG schemes have been developed in the present regulatory framework of the UK. However, as the power transfer capacity of distribution networks becomes saturated, there is an economic disadvantage to 'last-in' generators. This is because they are the first generators to be disconnected or have their power output constrained at times of power flow management. The resulting annual energy yield of such generators may be significantly curtailed and, based on the anticipated net present value of the investment, the DG development may not be economically viable. This paper builds on previous work by the authors which described the underlying principles of DG output control for network power flow management [START_REF] Jupe | Distributed generation output control for network power flow management[END_REF] and candidate strategies for the output control of multiple generators [START_REF] Jupe | Strategies for the control of multiple distributed generation schemes[END_REF] based on power flow sensitivity factors (PFSFs). PFSFs are derived from a full AC load flow solution and define the mathematical relationship between changes in network component power flows due to changes in DG power outputs. The candidate control strategies move away from piecemeal generator control systems to coordinate the power outputs of multiple generators in order to achieve aggregated benefits for power system stakeholders.

Under certain conditions the strategies have the potential to facilitate improved individual and aggregated annual energy yields for separately owned DG schemes [START_REF] Jupe | Strategies for the control of multiple distributed generation schemes[END_REF],

when compared to a benchmark LIFO DG tripping strategy. In such circumstances the coordinated output control of distributed generators could enhance the revenue streams of 'last-in' generators to an extent that the investment in the installation is economically viable. Moreover, cross-payments could be set-up between generators to ensure that those generators that constrain their power output to manage network power flows, facilitating an aggregated annual energy yield gain, are remunerated.

In situations where a viable assessment is made, power flows may be managed through the deployment of a DG power output control system coupled with component real-time thermal ratings (RTTRs). The adoption of RTTR systems is particularly relevant in applications where strong correlations exist between the cooling effect of environmental conditions and electrical power flow transfers. For example where high power flows resulting from wind generation at high wind speeds can be accommodated since the same wind speed has a positive effect on overhead line or power transformer cooling [START_REF] Belben | Aeolian uprating: how wind farms can solve their own transmission problems[END_REF]- [START_REF] Michiorri | An investigation into the influence of environmental conditions on power system ratings[END_REF].

The research described in this paper forms part of a UK government-funded project The concept of a delegated dispatch control centre has been developed in Spain to act as a mediator between the transmission system operator and a collection of wind farms connected to the same injection node [15]. Using a proactive control approach based on 15-minute operational forecasts, the delegated dispatch responds to system operator constraints imposed on the injection node. An optimisation problem is formulated considering power outputs of the generators, generator profit, busbar voltages and component thermal limits. In meeting the system operator constraints, the objective function aims to maximise generator aggregated profits. [20] for hydro turbines. In addition, the strategic benefits of DG ownership for DNOs is discussed by Siano et al. in [21] and the authors conclude that incentives need to be put in place to encourage DG deployment for the benefit of the distribution network.

This paper adds to the work above by proposing candidate strategies for the coordinated output control of multiple DG schemes in order to manage power flows within multiple components of the distribution network. This is of relevance in situations where individual generators may cause power flow excursions in individual components but of particular relevance in situations where the aggregation of power flows from multiple generators may cause more widespread power flow management issues. Therefore, with the expected proliferation of DG the resulting power flows are likely to affect many components within the distribution network and it is important to take a holistic view of power flow management. Since this research project aims to develop and deploy an economically viable on-line control system, it is important that algorithms are developed with fast computational speeds and have the capability of utilising real-time information about the thermal status of the distribution network.

Thus predetermined PFSFs are embedded within the control system for computational efficiency and a simulation tool is utilised to validate the control actions. Beyond the research described above, this paper also aims to quantify and compare the candidate strategies through a comprehensive set of evaluation parameters: DG individual and aggregated annual energy yields, generator-apportioned losses, DG development net present values and investment profitability indices, and busbar voltages. In addition, this paper also aims to quantify the impact of deploying the candidate strategies with different component rating systems, on the above mentioned evaluation parameters.

Power flow sensitivity factors

Underpinning this work is the theory of power flow sensitivity factors (PFSFs) that relate the changes in distribution network power flows to DG nodal power injections [START_REF] Jupe | Distributed generation output control for network power flow management[END_REF], [22]. The PFSFs are derived from the inverse Jacobian matrix, evaluated through a full AC load flow solution (1) [23].

                                            ΔQ ΔP Q V P V Q θ P θ ΔQ ΔP J ΔV Δθ 1 (1)
Thus the PFSFs for a real power injection at node m are calculated from ( 2)-( 3)

                   m , P i m , P k k , i m , P k , i dG d dG d P dG dP : ) ( f     (2)                    m , P i m , P k k , i m , P k , i dG dV dG dV V P dG dP : ) V ( f (3)
where f(θ) and f(V) represent functions of nodal voltage angles and nodal voltage magnitudes respectively, (dP/dθ) i,k and (dP/dV) i,k represent elements within the Jacobian matrix and dθ i /dG P,m , dθ k /dG P,m , dV i /dG P,m and dV k /dG P,m represent elements corresponding to the vector [START_REF] Strbac | Electric power systems research on dispersed generation[END_REF]. For a given operating condition, the evaluated PFSFs may be stored efficiently in matrix form [START_REF] Belben | Aeolian uprating: how wind farms can solve their own transmission problems[END_REF]. [START_REF] Belben | Aeolian uprating: how wind farms can solve their own transmission problems[END_REF] An assessment of the amount an individual generator is required to be constrained may be made using ( 5)-( 6) and PFSF values from the matrix M PFSF [START_REF] Belben | Aeolian uprating: how wind farms can solve their own transmission problems[END_REF].

      ΔV Δθ evaluated in
                   m , P k , i m , P 3 , 1 m , P 2 , 1 2 , P k , i 2 , P 3 , 1 2 , P 2 , 1 1 , P k , i 1 , P 3 , 1 1 , P 2 ,
         m , P k , i k , i m , P dG dP P G   (5)                          2 k , i 2 k , i 2 k , i 2 lim k , i Tar k , i Q ' S ' Q ' ' S U P  (6)
Thus the updated generator output is evaluated using ( 7)

m , P m , P ' m , P " G G G    (7)

Component ratings

Due to the variability and unpredictability of meteorological conditions, fixed seasonal assumptions are used to determine component ratings, S lim , which can be a conservative representation of actual operating conditions [START_REF] Michiorri | An investigation into the influence of environmental conditions on power system ratings[END_REF]. Difficulties associated with the maintenance of accurate seasonal rating databases often result in summer static rating utilisations throughout the year [10]. Moreover, the seasonal rating approach bears the latent risk of an anomalous 'hot day' where the prevailing meteorological conditions mean that components may be rated higher than they should be. For the purpose of this research, RTTRs are defined as a time-variant rating which can be practically exploited without damaging components or reducing their life expectancy. To calculate and exploit the RTTR, it is assumed that local environmental condition measurements are available as inputs to steady-state thermal models and that there are no outages (planned or unplanned) present within the electrical power system. Short-term transients, taking into account the thermal capacitance of power system components, are not included within the RTTR assessment. It is felt that this would not materially affect the GWh/annum throughput of energy within the electrical power system.

Proposed strategies for multiple DG control

This section presents the candidate strategies for power output control of multiple DG schemes.

LIFO PFSF-based

DG power outputs are curtailed in a LIFO contractual order, defined within the matrix

M LIFO (8)   id G m id G 2 id G 1 x x x   LIFO M (8)
where the integer x, represents the ranked order of DG curtailment for the generator, G, with a unique identifier (id), at nodes 1, 2, up to m respectively. The unique identifier aids clarity and is necessary for situations where multiple generators have the same connection point to the distribution network but separate operating contracts.

The generic form of this strategy is given in ( 9)-( 12). A set point change is dispatched to relevant DG operators that match DG power outputs to the capability of the network. If, by implementing the required reduction, as calculated in ( 9), the signal is driven negative (10) the DG is tripped (11) and the next generator, contractually, to be constrained is apportioned the required power output reduction (12). By adopting this approach, 'last-in' DG schemes are required to manage network power flows, when excursions occur, even though they may not technically be the most appropriate generators to do so.

         m , P k , i x k , i x m , P x dG dP P G   (9) If: 0 G G m , P x m , P x    (10) Then: 0 G m , P x  (11) and             m , P k , i x m , P x k , i x k , i 1 x dG dP G P P   (12)

Egalitarian broadcast

In this strategy a single percentage reduction signal, Φ, as calculated in ( 13)-( 14), is broadcast to all the relevant generators. When calculating the reduction signal, the power outputs of each generator are weighted by the associated PFSFs. The constraints required to manage network power flows are shared by each generator and those generators making a significant power output contribution are constrained more, in terms of the absolute power output reduction (ΔG P ) than those generators making a small contribution.

    m , P m , P G G (13)           m 0 m m , P ik m , P ik dG dP G P   (14)

Technically most appropriate

The curtailment of the generators is ranked, in a technical priority order, by the relative magnitude of PFSFs given in matrix M TMA (15)

  id G m id G 2 id G 1 x x x   TMA M (15)
where the integer x, represents the ranked order of DG curtailment for the generator, G, with a unique identifier (id) at nodes 1, 2, up to m respectively, based on the relative magnitudes of PFSFs given in [START_REF] Belben | Aeolian uprating: how wind farms can solve their own transmission problems[END_REF]. The generic form of this strategy implementation is given in ( 9)-( 12). In this case the DG with the best technical ability to manage network power flows is selected to be constrained first.

Quantification Techniques

In this section the techniques used to quantify the strategy evaluation parameters are given in generic forms. These include: numerical integration to calculate annual energy yields and annual energy losses, a loss apportioning technique to attribute energy losses to particular generators and the financial quantification of DG development net present values and profitability indices.

Numerical integration

The numerical technique used to integrate DG power outputs and hence quantify DG annual energy yields is given in ( 16)

               1 y x 1 x P x P y P 0 a G G G 2 1 t E (16)

Loss apportioning

The technique used to apportion energy losses to individual generators in a proportional manner, through a component connecting multiple DG schemes to the distribution network, is given in ( 17)-( 18)

m , P m , P total , k , i , loss m , k , i , loss G G P P    (17)                1 y x 1 x loss
x loss y loss 0 loss

P P P 2 1 t E (18)
More complex loss apportioning techniques, looking deeper into the power system, are described by Bialek [24] for power flow tracing and by Kirschen and Strbac [25] for current tracing.

Financial assessment

Building on the work of Payyala and Green [26], the methodology used to evaluate the net present value of the wind farm investment to each DG developer, and therefore the profitability index, is presented.

The total investment cost for each developer, C inv , is modelled as a sum of three variable costs ( 19)-( 22)

) n ( C ) ( C ) G ( C C 3 2 i 1 inv     (19)
where

install i i 1 C G ) G ( C   (20)
and G i represents the installed capacity of the wind farm and C install represents the total wind farm installation costs including wind turbine generators, foundations, electrical infrastructure, and planning and development costs;

  / C ) ( C control 2  (21)
where the cost of the control system, C control , including development costs, installation costs, maintenance costs, necessary communication links and the auxiliary trip system is shared amongst the number of stakeholder investors (N) and

n / C ) n ( C time real 3   (22)
where the cost of the RTTR system, C real-time , including development costs, thermal instrumentation costs, maintenance costs and the cost of necessary communication links is shared amongst the number of stakeholder investors, n.

The cost of the annual operations and maintenance, C OM , is modelled as a proportion, K, of the wind farm installation cost ( 23)

install i OM C G K C    (23)
The annual net revenue, R a , of each wind farm developer is modelled ( 24)-( 26) by subtracting the cost of losses, C loss , from the metered active annual energy yield revenue, Clearly the results of financial evaluations are sensitive to wind farm installation costs, discount rates, project lifetimes, wholesale electricity prices and the sale price of ROCs. In quantifying the NPVs and PIs a discount factor of 10% was assumed for a 20-year operational lifetime of the wind farm [27], the wholesale electricity price was assumed to be £52.15/MWh [28] and the trading price of ROCs was assumed to be £49.28/MWh [29]. The cost of the offshore wind farm installation was assumed to be £1000/kW and the costs of the onshore wind farm installations were assumed to be £800/kW [30]. Wind farm annual operations and maintenance costs were assumed to be 5% of the wind farm installation cost [31]. The cost of the power output control system (including project management and engineering design, hardware, software, installation, testing, commissioning and maintenance) was estimated to be £200k with the incorporation of component thermal monitoring equipment and £100k without.

R EY loss EY a C R R   (24)   ROC wholesale a EY C C E R    (25)

Case Study

Network description

A section of ScottishPower EnergyNetworks' distribution network, selected for RTTR field trials, is given in Figure 1 [32]. Additional generation was introduced at nodes B4 and B9 representing planned future connections of DG. Each DG scheme is separately owned and it was assumed that the generators' connection agreements contain the necessary clauses to allow operation outside of a LIFO constraint priority.

A summary of DG types and installed capacities is given in Table 1. An underlying meshed 33kV network was included in the network model for simulations but for simplicity is not presented. Through an off-line analysis of the network (which entailed the simulation of the generators with unconstrained outputs throughout the year), power flow excursions, with static thermal ratings, were found to occur in overhead line components C3, C5, C6, C7, C8 and C9. These components have been highlighted in Figure 1. The majority of overloads are caused by high wind generation, which can occur at high and low loads. Current levels of DG, operated with static thermal ratings, at present have a very small probability of causing overloads unless N-1 conditions are encountered. However, this work assesses overloads when considering planned future DG connections where the anticipated growth of DG increases, significantly, the probability of overloads occurring for system-intact operation with static thermal ratings.

Control approach

Rule-based decision making (inference) is an artificial intelligence technique [33] which has the potential to facilitate the automated control of systems. In this case the rule-based inference engine is designed to support the control decisions of DNOs. For the field trial network operating in normal conditions, the PFSF matrix (M PFSF ) was found to be of the form (29), based on the generalised matrix in [START_REF] Belben | Aeolian uprating: how wind farms can solve their own transmission problems[END_REF]. From left to right across each row, the terms relate to components C3, C5, C6, C7, C8 and C9. From top to bottom in each column the terms relate to DG7, DG8, DG1, DG2, DG3, DG4, DG5 and DG6.

                                               6 9 , 9 9 , 5 6
x x x x 0 x x x x 0 x x x x 0 x x x x 0 x x x x 0 x x x x 0 0 x x x x 0 0 x x x x LIFO M (30)
M PFSF was populated as given in (31) [START_REF] Jupe | Distributed generation output control for network power flow management[END_REF]. Therefore a series of 'If-Then' rules were created to establish the relationship between the power system components and the generators that it would be necessary to constrain to manage network power flows.

Considering (31), the zero terms in the matrix represent a negligible PFSF and indicate that the power output of a generator has negligible impact on the power flows in that particular component. By inspection of this matrix it is possible to see that the power flows in components C3 and C5 are affected only by generation at nodes B4 and B6 respectively. This is a fairly intuitive finding as these components are the feeder connections for the relevant distributed generators. Moreover, the power flow in component C9 is sensitive only to the outputs of generators connected at node B9.

Considering components C6, C7 and C8 it can be seen from Figure 1 that this is where power flows from all the distributed generators accumulate. Therefore the power flows in these components are sensitive to outputs from each distributed generator connected. Having identified these relationships the necessary rule-bases were created for the candidate control strategies. Considering Figure 1, an example rule-base is given in ( 34)-(37) for the necessary multiple DG constraints when implementing the egalitarian control strategy. where U represents the utilisation of a particular component and is defined as the ratio of apparent power flow to the thermal limit ('S i,k / S lim ) and S lim could be a static or real-time thermal rating.

Simulation approach

As a step towards the on-line control of multiple DG schemes, an off-line analysis was conducted to quantify the impact of the candidate control strategies on the evaluation parameters. Simulations used electrical data for the complete calendar year 2006, with a half-hourly data resolution. In order to reflect an element of diversity in the power injections, three different wind farm output profiles (two onshore and one offshore) were used for the analysis, each supplied by SPEN and based on historical data from the region being considered. DG1, DG2, DG3 and DG6 were modelled with one onshore profile, DG4 and DG5 were modelled with the other onshore profile and DG7 and DG8 were modelled with the offshore profile. These profiles were scaled by the relevant installed capacities to represent the future DG power outputs. The control system simulation, as shown in Figure 2, functions in the following manner: In the simulated deployment of the candidate control strategies within the field trial network, the topology and generator installed capacities were assumed to be constants of the system. Component ratings, types, cross-sectional areas and maximum operating temperatures are summarised in Table 2. Component RTTRs were computed with a half-hourly data resolution for the calendar year 2006 using the thermal models described in [START_REF] Michiorri | An investigation into the influence of environmental conditions on power system ratings[END_REF] and historical meteorological data for the 'Valley' area of Wales, UK [34]. Simulations were conducted with a target utilisation, U Tar = 0.95 and, rather than neglecting MVAr flow, it was assumed constant for a particular operating condition. Thus dP/dG P >> dQ/dG P and a simplification was made to [START_REF] Michiorri | An investigation into the influence of environmental conditions on power system ratings[END_REF] that "Q ≈ 'Q [START_REF] Jupe | Distributed generation output control for network power flow management[END_REF].

Discussion of results

Based on the datum annual energy yield values in Table 1 together with Figures 3a-3b, the adoption of egalitarian and TMA control strategies is particularly favourable for 'last-in' generators DG1-4 in terms of increased annual energy yields and hence revenue stream enhancement. Considering Figure 3a, the egalitarian control strategy deployed with component static ratings facilitates increased annual energy yields of 83.6% (25.8GWh), 45.7% (21.3GWh), 23.6% (16.9GWh) and 13.7% (12.0GWh) for DG1-4 respectively, through the reduction in annual energy yields of DG5 and DG6 by 7.4% (8.4GWh) and 10.5% (11.9GWh) respectively. The TMA control strategy facilitates increased annual energy yields of 116.6% (36.0GWh), 71.9% (33.6GWh), 45.9% (32.8GWh) and 31.3% (27.5GWh) for DG1-4 respectively, through the reduction in annual energy yields of DG5 and DG6 by 18.0% (20.6GWh) and 29.5% (33.6GWh) respectively. Considering Figure 3b, for each control strategy deployment with RTTRs, every generator sees an energy yield gain, and hence revenue stream enhancement. The only exception to this is DG6, if the TMA strategy is adopted. In this case there is a marginal 3.2% reduction in the annual energy yield of the DG scheme, when compared to the datum value of 113.9GWh in Table 1. As seen in ( 31), DG5 and DG6 have higher PFSFs, relative to DG1-DG4, therefore they are, technically, the most appropriate generators to constrain in order to manage power flows within C9.

Considering Table 3, based on the datum value of 943.8GWh/annum, by inspection of the data in each column it is possible to observe the aggregated annual energy yield gains that may be achieved by the adoption of more sophisticated candidate PFSFbased control strategies, deployed with the specified component thermal rating system. Similarly, by inspection of each row it is possible to observe the aggregated annual energy yield gains that may be achieved by adopting a more sophisticated component thermal rating system, deployed with the specified control strategy.

Respective aggregated annual energy yield gains of 6.5%, 7.1% and 11.0% may be achieved by the adoption of LIFO PFSF-based, egalitarian and TMA control strategies deployed with component static ratings. Therefore, the impact that coordinated power output control approaches have on individual generator revenue streams could mean that, even if the 'first-in' generators are remunerated for curtailing their power output at certain times of the year, there is an overall revenue gain for all the generators. Aggregated annual energy yield gains of 20.1% and 21.0% may be achieved by the respective adoption of basic DG tripping and the TMA control strategies deployed with component RTTRs. This represents increased aggregated energy yield of 190.1GWh/annum and 198GWh/annum beyond the datum value. As the component thermal rating system becomes more sophisticated the distinction between aggregated energy yields for the different candidate control strategies becomes less pronounced. However, the increased power transfer capacity that may be unlocked through component RTTR systems could lead to the accommodation of a larger installed capacity of distributed generation [START_REF] Michiorri | An investigation into the influence of environmental conditions on power system ratings[END_REF]. Therefore the adoption of coordinated DG power output control strategies could allow a greater percentage of the additional power transfer headroom to be realised.

The energy losses in C3 were apportioned directly to DG7, the losses in C5 were apportioned directly to DG8 and the losses in C9 were apportioned directly to DG1-6, based on ( 17)-( 18) in Section 4.2. As seen in Figure 4a, DG1-4 are apportioned additional annual energy losses (291MWh, 252MWh, 219MWh and 177MWh respectively) when the egalitarian control strategy is adopted. Additional annual energy losses of 421MWh, 409MWh, 423MWh and 380MWh are attributed to DG1-4 respectively in deploying the TMA control strategy. Inspection of Figure 4b shows that further additional annual energy losses are apportioned to all the generators in deploying the candidate control strategies with component RTTRs. The increase in losses resulting from coordinated control strategies are a direct result of increased power transfers and hence increased energy yields of the generators.

Tables 4 and5 summarise DG investment NPVs and PIs for the candidate control strategies deployed with component static and real-time thermal ratings respectively.

An NPV < 0 indicates an investment is not financially viable. Moreover, in evaluating the impact of candidate control strategies on the financial performance of the DG developments, a PI > 1 could be specified as the investment criterion. This indicates that the investor will recover at least double the cost of the initial investment over the project lifetime.

Considering the results presented in Table 4 for the LIFO DG trip control approach, it can be seen that if this approach is adopted then the investment would not be viable for DG1 since the NPV of the development is £-0.9M. This is because DG1 represents the last generator to connect to the network and therefore the first generator to be disconnected at times of network power flow management. The resulting impact on the annual energy yield of the generator means that insufficient revenue is earned over the project lifetime to justify the initial investment cost. The LIFO PFSF-based approach is most preferable for DG5-8, in terms of revenue stream enhancement (with respective NPV gains of £34.4M, £33.8M, £105.8M and £151.6M.), since they are the 'first-in' generators. The egalitarian strategy enhances the revenue streams and hence increases the profitability indices of DG1-4 and DG7. The respective NPV gains for these generators were £41.4M, £42.3M, £45.7M, £42.3M and £112.2M. The technically most appropriate control strategy resulted in the greatest enhancement to the revenue streams of DG1-4 (with respective NPVs of £49.2M, £58.6M, £76.2M and £85.5M), due to the coordinated power output control of DG5-8 at times of power flow management. Considering Table 5, it can be observed that the candidate control strategies deployed with component RTTRs all display a similar financial performance (the largest NPV difference being £8.6M for DG6 without remuneration). The LIFO PFSF-based strategy is marginally favourable for DG5-8 (with respective profitability indices of 1.9, 2.4, 1.8 and 1.8) and the technically most appropriate strategy is marginally favourable for DG1-4 (with respective PIs of 2. 4, 2.3, 2.4 and 2.6).

In all cases the busbar voltages conformed to the statutory UK requirements specified in [35]. Furthermore, for the simulated year, the voltage profiles of the busbars at the GSPs represented the extremities of voltage excursions away from nominal and all other busbar voltages remained within these bounds. The maximum per unit voltage difference between the LIFO DG tripping approach and the TMA strategy, deployed with component static thermal ratings, occurred at node B9 and was found to be 0.4%. This was attributed to a voltage rise effect [36] along the feeder. The maximum per unit voltage difference between TMA strategy deployments with component static and RTTRs also occurred at node B9 and was found to be 0.9%.

Discussion of control parameters and approaches

The proposed control techniques make use of predetermined PFSFs which have been shown to be a valid linear approximation of the network power flow management problem [START_REF] Jupe | Distributed generation output control for network power flow management[END_REF]. As seen in ( 5) there is an inverse relationship between the magnitude of PFSFs and the extent to which generators are constrained in order to manage network power flows. PFSFs are a function of the complex impedances of components within the electrical network as embodied in the Jacobian matrix. The connection of DG to electrically strong distribution networks with low impedance paths lead to high PFSFs. In weaker electrical networks, such as those found in rural parts of the UK, the long electrical feeders result in high electrical impedances. As a result there are greater electrical losses and lower PFSFs.

Distribution network topology changes have the potential to impact on the continued operation of the DG output control system particularly if the magnitude of PFSFs is affected. The derived PFSFs are network topology and network configuration specific and, in simulating the proposed control strategies it was assumed that the network topology was constant. It is feasible, however, to develop PFSF-based control strategies that make use of alternative sets of the above-mentioned predetermined PFSFs, based on network switch information. For each configuration a new off-line analysis would be required to determine the PFSFs. Alternatively, PFSFs could be calculated in real-time and used to update values within the control matrix M PFSF .

Network extensions and new DG connections are planned by the DNO many months in advance. The off-line methodology required to adapt the DG output control system to deal with these network topology changes is provided in [START_REF] Jupe | Distributed generation output control for network power flow management[END_REF]. An overview of the required control system modification is outlined below: It should be noted that some network topology changes may have a negligible impact on the magnitude of PFSFs and therefore control system modifications may not be necessary.

It is anticipated that the utilisation target, U Tar , would be defined by the DG developer or DNO to represent the factor of safety, or risk, that the DNO is prepared to accommodate in terms of operating the relevant power system component. To minimise the risk of power flow excursions beyond the transfer capacity of components, and thus to ensure the safe and secure operation of the distribution network, it is expected that the candidate control strategies would be deployed with an auxiliary trip system [17]. With the functionality to incorporate the same component rating systems as the primary control system, the auxiliary system acts as a backup in the case of control system operation failure, communications failures or the failure of DG schemes to match the updated set points within the required time frame.

The DG output control system makes use of an on-line simulation tool that has the capability of validating operational voltages against operational limits. If voltage limits were to become a constraining factor, this would currently need to be dealt with outside of the jurisdiction of the DG output control system using active voltage techniques as demonstrated in [37]. Alternatively, the functionality of the control system could be extended to make use of voltage sensitivity factors, as discussed in [23,38]

Conclusions

This paper attempts to quantify the benefits in adopting candidate PFSF-based strategies for the future coordinated output control of multiple distributed generators.

It is shown that, in certain circumstances, there are significant benefits to individual generators in terms of energy yields and hence revenue streams by moving away from LIFO control strategies. As a result the aggregated annual energy yield of separately owned generators is considerably improved. The impact that coordinated power output control approaches have on individual generator revenue streams could mean that, even if the 'first-in' generators are remunerated for curtailing their power output at certain times of the year, there is an overall revenue gain for all the generators.

Although the case study presented in this paper is UK-based, the strategies, simulation approach and research outcomes are transferable to networks internationally. Whilst, in the UK there are no mechanisms in place at present to encourage and reward an increase in aggregated energy yield contributions from separately owned distributed generations, there are examples in Europe where this concept is recognised and is beginning to be adopted [14]- [15].

Clearly, the illustrative figures, relating to financial assessments, vary with time and location. Variations in wind farm installation and operating costs would impact on the NPVs and PIs of the DG scheme developments, and hence the investment decision. If these values were to differ from the illustrative costs used in this paper, the principle of the analysis would still remain valid. The proposed methodology may be used with figures that are most appropriate to the particular situation being considered.

In light of the results and discussions presented in this paper, it is recommended that any DNO or DG developer looking to adopt the proposed PFSF-based strategies should conduct an off-line analysis to assess the value of output control of multiple DG schemes. This is because the control strategy implementations are a function of a number of site-specific control variables and therefore the economic value in each case is different. Work is continuing in this area to realise the potential of coordinated output control strategies for multiple DG schemes.

Vn

  static or real-time) of the component from node i to node k (MVA) 'S i,k Apparent power flowing from node i to node k before control actions are implemented (MVA) U i,k Utilisation of component between node i and node k U Tar Target utilisation of component after control actions have been implemented Vector of nodal voltages (kV) dP i,k / dG P,m Power flow sensitivity factor representing the change in real power flow from node i to node k due to change in real power output of generator connected to node m Number of stakeholder investors in real-time thermal rating system t Integration time-step (h) x Ranked order of constraint for a distributed generator ΔG P,m Required change in real power output of the generator, G, at node m (MW) ΔP i,k Required change in real power flowing from node i to node k for network power flow management (MW)

[ 7 ]

 7 , in conjunction with AREVA T&D, Imass, PB Power and ScottishPower EnergyNetworks (SPEN), which aims to manage, actively, DG based on component thermal properties. The on-line control system compares component RTTRs with network power flows and produces set points that are fed back to the DG operator for implementation. A section of SPEN's distribution network has been selected for field trials where electrical and thermal monitoring equipment has been installed to allow open loop validation of the algorithms developed. The paper is structured in the following way: Section 2 provides a survey of the current techniques adopted for the power output control of DG and outlines the background to control techniques based on PFSFs, informed by component thermal properties. Section 3 proposes three candidate strategies (LIFO, egalitarian and technically most appropriate) for the output control of DG based on PFSFs. Section 4 describes the generic forms of the techniques used to quantify the candidate control strategy evaluation parameters. Section 5 describes the off-line simulation of the candidate control strategies, implemented to manage power flows within a section of the UK power system. Simulation results are presented and discussed in Section 6 and the impact of the candidate control strategies on evaluation parameters is quantified. Where appropriate, the results are expressed as marginal values based on a datum control strategy corresponding to a LIFO DG tripping approach deployed with component static ratings. In light of the findings, recommendations are made regarding the suitability of the control strategies for deployment with different component thermal rating systems. Section 7 discusses the use of PFSFs and the relative merits of the candidate strategies for DG output control. In the UK, solutions for the power flow management of single distributed generators have been proposed by the Energy Networks Association [8] as a distillation of a report by the Distributed Generation Coordinating Group's Technical Steering Group [9]. DG power output control is achieved through network availability assessments by tripping (disconnection) or a demand-following strategy with auxiliary tripping. The latter strategy has the potential to utilise short-term component thermal ratings. Additionally, Roberts [10] considers the feasibility of incorporating the proposed solutions within a supervisory, control and data acquisition (SCADA) system for distribution network operators (DNOs).Recent research investigates the constrained connection of multiple distributed generators on the island of Orkney[11]. A 'trim and trip' DG output control strategy is adopted and embedded in programmable logic control[12]. Reflecting present operational practices in the UK, a LIFO constraint priority defines the order in which generators are controlled.As part of the AuRA-NMS project,Dolan et al. [13] present two techniques for the management of power flows within static thermal constraints. The techniques are illustrated through the control of DG within an 11kV distribution network and are assessed in terms of algorithm computational times and impact on DG curtailments. It is shown that the current-tracing technique marginally achieves the least DG real power curtailment but that the constraint satisfaction problem technique is more computationally efficient and allows contractual constraints to be considered.Kabouris and Vournas [14] demonstrate the on-line development of interruptible wind farm contracts to manage the power flow through a congested corridor of the Hellenic Interconnected System in Greece. When security constraints are violated, the control of multiple DG schemes is achieved through the proportional reduction of generators' power output or by distributing generator curtailments according to a continuously updated priority list. Both proactive (pre-outage) and reactive (post-outage) control concepts are developed and illustrated based on a static security assessment of the available transfer capacity.

  present value (NPV) of each wind farm investment is quantified (27) by assessing the present value (PV) of the annuity (R a -C OM ), discounted over the project lifetime, and subtracting the cost of the original investment.

  profitability index (PI) for each DG developer is defined as the ratio of the NPV to the initial investment (28)[26]

  LIFO DG constraint matrix (M LIFO ) and the technically most appropriate DG constraint matrix (M TMA ) were found to be of the form (30).

  in (32), based on (8) and DG contractual mechanisms. In addition,MTMA is given in (33), based on (15) and (31).

1 .

 1 Grid supply point (GSP) reference voltages and power flows are input to the 'distribution network simulator' and 'offline simulation tool' both of which are load flow algorithms (a); 2. Normalised historical load demand and generation power output profiles are scaled through multiplication by peak values (b)-(c) respectively, which are also input to the 'distribution network simulator' and 'offline simulation tool' (d)-(e); 3. Component static and real-time thermal ratings are fed into the 'rule-based inference engine' (f) together with a full set of component power flows which have been computed by the 'distribution network simulator' (g). Based on the ranked magnitude of component utilisations together with embedded knowledge of the ability of DG to control component power flows (signified by non-zero values within M PFSF ), the 'rule-based inference engine' decides if a control action is necessary and which DG scheme(s) should be constrained. If a control action is required then the ΔP i,k value is passed to the 'DG set point calculator' (h). If no action is required then the 'offline analysis tool' records the present DG power outputs, component losses, component utilisations and busbar voltages (k) and the control system reads in data for the next ½ hour interval; 4. The 'DG set point calculator' receives information from the 'rule-based inference engine' regarding the necessary real power flow reduction as well as an indication of which DG schemes have the ability to manage the network power flows. Using a look-up table of predetermined PFSFs (M PFSF ), updated DG set points are calculated depending on the candidate control strategy selected; 5. New DG set point values are passed to the 'offline simulation tool' (i) and together with GSP reference voltages, reference power flows and load demands, an updated load flow is computed; and 6. The updated sets of complete power flows and busbar voltages are passed back to the inference engine (j). This validates that all power flows and voltages are within designated limits. Steps 3 -6 are repeated with the updated DG set point values until all the power flows within the network are brought back within thermal limits.

1 .

 1 Modify the topology of the network, as appropriate, in the off-line analysis software and on-line simulation tool; 2. Conduct an new off-line study to identify any new thermally vulnerable components within the distribution network; 3. Develop, as appropriate, a new real-time thermal rating system to incorporate new thermally vulnerable components; and 4. Specifically related to the control algorithm, each control strategy would require updating in the following manner: a. Determine updated PFSFs; b. Modify the rule-base in the inference engine to achieve desired control functions; c. Incorporate additional terms in the DG set point calculator equations; and d. Update the on-line simulation tool to maintain the integrity of the power flow and voltage validation tasks.

  . The work in [38] is of particular relevance as it considers the coordinated output control of multiple DG schemes by use of voltage sensitivity factors. LIFO strategies represent the present UK practice whereby 'last-in' generators are constrained for network power flow management. If management issues become more widespread 'last-in' generators may not be, technically, the most appropriate to constrain. Moreover, there is an increased complexity for DNOs in terms of dispatching constraint signals. The egalitarian broadcast strategy overcomes signal dispatching complexities. All technically relevant generators are controlled to manage network power flows and this has the potential to facilitate aggregated annual energy gains. The TMA strategy utilises generators with the best technical ability to manage network power flows. This has the potential to lead to the greatest aggregated annual energy yield gains of the proposed PFSF-based strategies. However, there is an associated signal dispatching complexity as DG proliferates and network management issues become more widespread. In situations where the PFSFs are of similar magnitude there is little merit in applying the technically most appropriate control strategy. However, the egalitarian strategy would still have the potential to allow increased installed capacities of intermittent generation thereby impacting on both individual and aggregated annual energy yields. The control system uses the DNO's SCADA signals for electrical monitoring and DG output control. Therefore, moving from LIFO-based to non-LIFO-based control strategies has no added communication requirements when deployed with static thermal ratings. The step which would entail extra communication links is the implementation of RTTR systems. In research, also carried out Durham University as part of this project, thermal state estimation techniques have been developed and validated to estimate RTTRs in wide areas of the distribution network based on limited monitoring equipment and communication link installations [39].

  Figure 1. Field trial network topology

Table 4 -

 4 Wind farm financial evaluation (static thermal ratings)

		Control				Generator
		strategy	DG1 DG2 DG3 DG4 DG5 DG6 DG7 DG8
	NPV	LIFO trip	-0.9	5.9	16.9	29.9	34.6	46.5	49.0	64.4
	(£M)
		LIFO PFSF-	19.8 30.2	48.9	67.2	79.0	80.3	154.8 216.0
		based
		Egalitarian	40.5 48.2	62.6	72.2	72.1	72.3	161.2 190.4
		Technically	49.2 58.6	76.2	85.5	61.7	53.6	150.9 187.4
		most
		appropriate
	PI	LIFO trip	-	0.2	0.5	0.9	0.8	1.3	0.5	0.5
		LIFO PFSF-	1.0	1.2	1.5	2.0	1.7	2.2	1.7	1.8
		based
		Egalitarian	2.0	1.9	1.9	2.2	1.6	2.0	1.8	1.6
		Technically	2.4	2.3	2.4	2.6	1.3	1.4	1.7	1.6
		most
		appropriate

Table 5 -

 5 Wind farm financial evaluation (real-time thermal ratings)

		Control				Generator
		strategy	DG1 DG2 DG3 DG4 DG5 DG6 DG7 DG8
	NPV	LIFO trip	22.0 28.9	41.2	52.9	43.3	51.8	72.4	96.7
	(£M)
		LIFO PFSF-	41.8 53.0	72.7	85.8	88.0	87.8	162.6 217.0
		based
		Egalitarian	47.6 56.7	73.8	84.2	84.8	85.1	162.2 215.5
		Technically	49.2 58.6	76.2	87.2	84.6	79.2	161.5 215.9
		most
		appropriate
	PI	LIFO trip	1.1	1.2	1.3	1.6	0.9	1.4	0.8	0.8
		LIFO PFSF-	2.1	2.1	2.3	2.6	1.9	2.4	1.8	1.8
		based
		Egalitarian	2.4	2.3	2.3	2.5	1.9	2.3	1.8	1.8
		Technically	2.4	2.3	2.4	2.6	1.8	2.1	1.8	1.8
		most
		appropriate

9 Acknowledgements This work was supported by AREVA T&D, Imass, PB Power and ScottishPower EnergyNetworks with funding from the Department for Innovation, Universities and Skills, the UK Engineering and Physical Sciences Research Council and Durham University.

Tables

 Table 1 -