Samuel C E Jupe

Andrea Michiorri

Philip C Taylor

Increasing the energy yield of generation from new and renewable energy resources

Introduction

The impetus of governments, on an international scale, to move towards low-carbon economy targets has brought about the proliferation of electricity (and heat) generation from new and renewable energy (RE) resources. This, coupled with increasing consumer energy demands, has caused distribution network operators (DNOs) to seek methods of increasing the utilisation of their existing power system assets. The increased utilisation of assets must be realised cautiously such that the security of supply to customers is not reduced, particularly when the age of distribution network assets is taken into account. A developer that is seeking to connect generation of significant capacity may be offered a firm connection by the DNO on the condition that an investment is made (by the developer) in the necessary network reinforcements. However, the developer may not be able to justify the expense of the required reinforcement and may negotiate a non-firm or "constrained" connection agreement, whereby the generation installation is tripped off or constrained back under certain network operating conditions. Furthermore, difficulties may be encountered when attempting to gain permission to build network infrastructure, in order to accommodate new generation installations, 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 real-time thermal rating systems which have the potential, in certain circumstances, to be both less invasive and more cost effective when compared to network reinforcement options. Non-firm generation connections are expected to occur more frequently as network power flow congestion occurs. Therefore the deployment of a power output control system, informed by real-time thermal ratings, may be required to increase the energy yield of generation from new and RE resources.

The stages in the development of an output control system for generation installations are illustrated. Section 2 provides a comprehensive literature review in order to provide the context for the research presented. Section 3 describes the assessment of the location of power flow congestion within the power system (due to the proliferation of generation from new and RE resources) so as to facilitate the targeted development of thermal models for thermally vulnerable components. This is achieved through the calculation of thermal vulnerability factors that relate power flow sensitivity factors (derived from governing alternating current power flow equations) to component steady-state thermal limits. Section 4 describes models for the steady-state assessment of power system component realtime thermal ratings. Industrial standard lumped parameter models are described for overhead lines, electric cables and power transformers. In a consistent manner, these models allow the influence of environmental conditions (such as wind speed) on component real-time thermal ratings to be assessed. Section 5 describes thermal state estimation techniques that allow the rating of components, which are not directly monitored within the power system, to be assessed. Thermal state estimations facilitate the precise and reliable assessment of environmental conditions whereby a minimal number of meteorological monitoring allows the thermal state of components within a wide area to be assessed. This may then be validated through the carefully selected monitoring of component operating temperatures. In Section 6, the power flow sensitivity factors are brought together with component real-time thermal ratings and candidate strategies are presented for the power output control of single or multiple generation installations. In Section 7, a case study is used to illustrate the developmental stages described above. In Section 8 the strengths and weaknesses of the proposed output control system for generation installations are discussed.

The research described in this chapter forms part of a UK government part-funded project [START_REF] Neumann | Dynamic thermal rating and active control for improved distribution network utilisation[END_REF] which aims to develop and deploy an online power output controller for wind generation installations through the exploitation of component real-time thermal properties. This is based on the concept that high power flows resulting from wind generation at high wind speeds could be accommodated since the same wind speed has a positive effect on component cooling mechanisms. In this project the control system compares component real-time thermal ratings with network power flows and produces set points that are fed back to the generation scheme operator for implementation, as shown in Fig. 1. (2)

(2)

(1)

G Generation installation

Fig. 1 Generation power output controller informed by real-time thermal ratings

Background

Installed capacity assessments for generation from new and RE resources are the current research focus of numerous institutions in order to determine the impact of voltage regulations, operational economics, fault levels, losses and thermal limits as constraining parameters. Voltage limitations and the wind farm installed capacity relative to the system fault level in 33kV networks are considered [START_REF] Dinic | Increasing Wind Farm Capacity[END_REF] and it is concluded that capacitive compensation can allow capacity maximisation within operational limits. The economics of generation connections to exploit multiple new and RE resources are considered [START_REF] Currie | Methodology for determination of economic connection capacity for renewable generator connections to distribution networks optimised by active power flow management[END_REF] with a methodology that facilitates greater generation installed capacities. In order to manage power flows within prescribed voltage and thermal limits, operating margins are utilised with an active power output control technique termed "trim then trip".

An optimal power flow (OPF) technique is developed [START_REF] Vovos | Optimal Power Flow as a Tool for Fault Level-Constrained Network Capacity Analysis[END_REF] along with an iterative procedure to calculate the possible installed capacity of generation at nodes based on fault level limitations. The impact of increased generation installed capacities on electrical losses within the IEEE 34-node test network is examined [START_REF] Mendez Quezada | Assessment of Energy Distribution Losses for Increasing Penetration of Distributed Generation[END_REF] and it is concluded that losses follow a U-shaped trajectory when plotted as a function of the generation penetration. An OPF formulation is presented (Harrison & Wallace, 2005) to determine the maximum generation installed capacity based on thermal limits and statutory voltage regulation. The "reverse load-ability" methodology coupled with OPF software models generators as loads with a fixed power factor and creates an analysis tool that could allow additional constraints (such as fault-level limitations) to be incorporated into the formulation if necessary.

Significant research has been carried out at the transmission level for real-time thermal rating applications. Research tends to focus on overhead lines which, due to their exposure to the environment, exhibit the greatest rating variability. A description of the cost and suitability of different uprating techniques for overhead lines is described [START_REF] Stephen | Description and evaluation of options relating to uprating of overhead transmission lines[END_REF] taking into account different operating conditions. This work shows how real-time thermal ratings can be a more appropriate solution than network reinforcement when connecting new customers to the network who are able to curtail their generation output or reduce their power demand requirement at short notice.

Similarly, experience regarding thermal uprating in the UK is reported [START_REF] Hoffmann | The approach to thermal uprating of transmission lines in the UK[END_REF] where it was suggested that real-time thermal ratings could give overhead lines an average uprating of 5% for 50% of the year. An example of a real-time thermal rating application for transmission overhead lines of Red Eléctrica de España is described [START_REF] Soto | Increasing the capacity of overhead lines in the 400 kV Spanish transmission network: real time thermal ratings[END_REF] where a minimal amount of weather stations are used to gather real-time data. The data is then processed using a meteorological model based on the Wind Atlas Analysis and Application Program (WAsP), taking into account the effect of obstacles and ground roughness, and the thermal rating is calculated. A similar system was developed in the USA by EPRI [START_REF] Douglass | Real-time monitoring and dynamic thermal rating of power transmission circuits[END_REF] which considered overhead lines, power transformers, electric cables and substation equipment. Preliminary results of field tests [START_REF] Douglass | Field application of a dynamic thermal circuit rating method[END_REF] show that up to 12 hours of low wind speeds (<0.76 ms -1 ) were observed during the field tests which therefore suggests that overhead line real-time thermal ratings may be lower than seasonal ratings for extended periods of time. Furthermore, a strong correlation was found to exist between independent air temperature measurements distributed along the lengths of the overhead lines. At the distribution level, a real-time thermal rating project carried out by the Dutch companies NUON and KEMA [START_REF] Nuijten | Future network planning and grid control[END_REF] demonstrates the operating temperature monitoring of overhead lines, electric cables and power transformers.

The advantages of a real-time thermal rating system for the connection of generation from new and RE resources are reported in various sources, each of which considers only single power system components. It is demonstrated [START_REF] Helmer | Optimized size of wind power plant transformer and parallel operation[END_REF] that the rating of transformers positioned at the base of wind turbines may presently be oversized by up to 20%. Moreover, the power flowing in an overhead line close to a wind farm is compared to its real-time thermal rating using WAsP [START_REF] Belben | Aeolian uprating: how wind farms can solve their own transmission problems[END_REF]. In this research it was highlighted that high power flows resulting from wind generation at high wind speeds could be accommodated since the same wind speed has a positive effect on the line cooling. This observation makes the adoption of real-time thermal rating systems relevant in applications where strong correlations exist between the cooling effect of environmental conditions and electrical power flow transfers. Moreover, the influence of component thermal model input errors on the accuracy of real-time thermal rating systems is studied [START_REF] Piccolo | Thermal rating assessment of overhead lines by Affine Arithmetic[END_REF][START_REF] Ippolito | The use of affine arithmetic for thermal state estimation of substation distribution transformers[END_REF][START_REF] Villacci | Transient tolerance analysis of power cables thermal dynamic by interval mathematic[END_REF]. The application of different state estimation techniques, such as affine arithmetic, interval arithmetic and Montecarlo simulations was studied for overhead lines, electric cables and power transformers. Errors of up to ±20% for an operating point of 75 o C, ±29% for an operating point of 60 o C and ±15% for an operating point of 65 o C were found when estimating the operating temperature of overhead lines, electric cables and power transformers respectively. This highlights the necessity to have reliable and accurate environmental condition monitoring. The thermal models, used to estimate real-time thermal ratings for different types of power system components, are fundamental to this research as the accuracy of the models influence significantly the accuracy of real-time thermal ratings obtained. Particular attention was given to industrial standards because of their wide application and validation both in industry and academia.

For overhead lines, the models [START_REF] House | Current Carrying Capacity of ACSR[END_REF][START_REF] Morgan | The thermal rating of overhead-line conductors[END_REF] have been developed into industrial standards by the IEC (IEC, 1995), CIGRE (WG 22.12, 1992) and IEEE (IEEE, 1993). Static seasonal ratings for different standard conductors and for calculated risks are provided by the Electricity Network Association (ENA, 1986). Thermal model calculation methods for electric cable ratings are described [START_REF] Neher | The calculation of the temperature rise and load capability of cable systems[END_REF] and developed into an industrial standard by the IEC [START_REF] Iec | IEC 60826: Loading and strength of overhead transmission lines[END_REF]. The same models are used by the IEEE (IEEE, 1994) and the ENA (ENA, 2004) to produce tables of calculated ratings for particular operating conditions. Power transformer thermal behaviour is described [START_REF] Susa | Dynamic thermal modelling of power transformers[END_REF] with further models described in the industrial standards by the IEC (IEC, 2008), the IEEE (ANSI/IEEE, 1981) and the ENA (ENA, 1971).

The work detailed in this chapter moves beyond the offline assessment of generation installed capacities to outline the development stages in the online power output control of generation installations. The thermal vulnerability factor assessments presented in this chapter complement network characterisation methods [START_REF] Berende | Incorporating Weather Statistics in Determining Overhead Line Ampacity[END_REF] by first identifying the type (overhead line, underground cable, power transformer) and geographical location of thermally vulnerable components. The assessments may be used to give a holistic network view of the impact of multiple generation installations in concurrent operation on accumulated power flows and hence vulnerable component locations. This facilitates the targeted development of component thermal models. Moreover, [START_REF] Michiorri | An investigation into the effect of environmental conditions on power system ratings[END_REF] describes the influence of environmental conditions on multiple power system component types simultaneously. This is of particular relevance in situations where the increased power flow resulting from the alleviation of the thermal constraint on one power system component may cause an entirely different component to constrain power flows. Whilst OPF is acknowledged as a powerful tool for the offline planning of electrical networks, there is an emerging requirement to manage non-firm generation connections in an online manner. This requires the deployment of a system which has the capability of utilising real-time information about the thermal status of the network and, in reaching a control decision, guarantees that the secure operation of the distribution network is maintained. The rapid processing time, reduced memory requirements and robustness associated with embedding predetermined power flow sensitivity factors in a power output control system for generation installations make it attractive for substation and online applications. This is strengthened further by the ability of the power output control system to readily integrate component real-time thermal ratings in the management of network power flows for increased new and renewable energy yields. Moreover, since this research project aims to develop and deploy an economically viable real-time thermal rating system, it is important that algorithms are developed with fast computational speeds using a minimal amount of environmental condition monitoring. Thus an inverse distance interpolation technique is used for modelling environmental conditions across a wide geographical area, which offers faster computational speeds than applications such as WAsP.

Beyond the research described above, this chapter also suggests potential annual energy yields that may be gained through the deployment of an output control system for generation installations.

Power flow sensitivity factors

Once the inverse Jacobian has been evaluated in the full AC power flow solution, perturbations about a given set of system conditions may be calculated using Eq.1 [START_REF] Wood | Power Generation, Operation and Control[END_REF]. This gives the changes expected in bus voltage angles and voltage magnitudes due to injections of real or reactive power.

  ΔQ ΔP ΔQ ΔP J V Δ Δθ V Δ Δθ k k i i 1 - k k i i                                    (1)
Where θ i and θ k (rad) represent voltage angles at nodes i and k respectively, |V i | and |V k | (kV) represent nodal voltages, J is the Jacobian matrix, P i and P k (MW) represent real power injections at nodes i and k respectively and Q i and Q k (MVAr) represent reactive power injections at nodes i and k respectively.

The work presented in this paper is specifically concerned with calculating the effect of a perturbation of ΔP mthat is an injection of power at unity power factor (real power) into node m. Since the generation shifts, the reference (slack) bus compensates for the increase in power. The Δθ and Δ|V| values in Eq.2 are thus equal to the derivative of the bus angles and voltage magnitudes with respect to a change in power at bus m.

 

ΔQ ΔP ΔQ ΔP J V Δ Δθ ref ref m m 1 -                       (2)
Thus the sensitivity factors for a real power injection at node m are given in Eq.3-6:

                        m , P i m , P k k , i m , P k , i dG d dG d P dG dP : f (3)                     m , P i m , P k k , i m , P k , i dG V d dG V d V P dG dP : ) V ( f (4)                         m , P i m , P k k , i m , P k , i dG d dG d Q dG dQ : f (5)                       m , P i m , P k k , i m , P k , i dG V d dG V d V Q dG dQ : V f (6)
Where f(θ) and f(V) represent functions of voltage angles and voltage magnitudes respectively, (∂P/∂θ) i,k , (∂P/∂V) i,k , (∂Q/∂θ) i,k and (∂Q/∂V) i,k , and represent elements within the Jacobian matrix and dθ k /dG P,m , dθ i /dG P,m , dV k /dG P,m and dV i /dG P,m represent elements corresponding to the relevant Δθ and Δ|V| values evaluated in Eq.2. This gives an overall power flow sensitivity factor (S i,k,m ) in the component from node i to node k, due to an injection of real power, at node m, as in Eq.7.

                                                                                                                                          m , P i i m , P k k k , i m , P i m , P k k , i m , P i i m , P k k k , i m , P i m , P k k , i c m , k , i dG E E d dG E E d E E Q dG d dG d Q j dG E E d dG E E d E E P dG d dG d P SSF (7)
Simplified versions of the power flow sensitivity factor theory (focusing on the P-θ sensitivity) are used at the transmission level for real power flow sensitivity analyses. The generation shift factor (GSF) technique [START_REF] Wood | Power Generation, Operation and Control[END_REF] is acceptable for use in DC representations of AC systems where the network behaviour is approximated by neglecting MVAr flow and assuming voltage to be constant. However, in distribution networks those assumptions do not always hold since, in some cases, the electrical reactance (R E ) and electrical resistance (X) of components is approximately equal (i.e. X/R E ≈ 1). Thus reactive power flow may contribute to a significant portion of the resultant power flowing in components. In these situations it is important that both real and reactive power flows are considered when assessing the locations of thermally vulnerable components and developing techniques for the online power output control of generation from new and RE resources.

Thermal vulnerability factors

Eq.7 may be combined with the relevant component thermal rating and the resulting thermal vulnerability factor, as given in Eq.8, is standardised by conversion to a per unit term on the base MVA

           lim m , k , i m , k , i S SSF TVF on S base (8)
where TVF i,k,m represents the thermal vulnerability factor of the component from node i to node k due to a real power injection at node m, SSF i,k,m represents the power flow sensitivity factor in the component from node i to node k, due to a real power injection at node m, S lim (MVA) represents the thermal limit of the component and S base is a predefined MVA base. This gives a consistent measure of component thermal vulnerabilities, relative to one another and accounts for different nodal real power injections, for a particular network operating condition. It can also be seen in Eq.9 that the sensitivity factor relative to the component rating is equivalent to the change in utilisation of a particular component from node i to node k, due to an injection of real power at node m

                                 P, m i,k lim P, m i,k lim i,k,m ΔG ΔU S ΔG ΔS S SSF (9)
where SSF i,k,m represents the power flow sensitivity factor of the component, from node i to node k, due to a real power injection at node m, S lim (MVA) represents the thermal limit of component, ΔS i,k (MVA) represents the change in apparent power flow in the component from node i to node k, ΔG P,m (MW) represents the change in real power injection at node m and ΔU i,k represents the change in capacity utilisation of the component from node i to node k.

Power flow sensitivity factors indicate the extent to which power flow changes within components due to nodal power injections. However, a large change in power flow, indicated by high sensitivity, does not necessarily mean a component is thermally vulnerable unless its rating is taken into account.

A large power flow change in a component with a large thermal rating could be less critical than a small power flow change in a component with a small rating. By calculating the apparent power sensitivity relative to rating for each component, the thermally vulnerable components are identified and can be ranked for single nodal power injections or accumulated for multiple injections.

Factor assessments

An empirical procedure [START_REF] Jupe | Distributed generation output control for network power flow management[END_REF] to assess power flow sensitivity factors and generate lists of thermally vulnerable components for different network topologies has been developed as follows: Initially a "base case" AC load flow is run in the power system simulation package to establish real, reactive and apparent power flows for each component. The procedure iterates by injecting 1pu of real power at each node of interest and recording the new component power flows.

The initial flow, final flow and thermal rating of each component are used to relate component power flow sensitivity factors to nodal injections and ratings. The resulting power flow sensitivity factors and thermal vulnerability factors are efficiently stored in matrix form and, with the thermal vulnerability factors represented graphically, a visual identification of the most thermally vulnerable components is given.

Thermal modelling approach

In order to assess, in a consistent manner, component real-time thermal ratings due to the influence of environmental conditions, thermal models were developed based on IEC standards for overhead lines [START_REF] Iec | IEC 60826: Loading and strength of overhead transmission lines[END_REF], electric cables (IEC, 1994) and power transformers (IEC, 2008). Where necessary, refinements were made to the models (WG22. 12, 1992;ENA, 2004). Steady-state models have been used in preference to dynamic models since this would provide a maximum allowable rating for long term power system operation.

Overhead lines

Overhead line ratings are constrained by a necessity to maintain statutory clearances between the conductor and other objects. The temperature rise causes conductor elongation which, in turn, causes an increase in sag. The line sag, ψ (m), depends on the tension, H (N), the weight, mg (N) applied to the conductor inclusive of the dynamic force of the wind and the length of the span, L (m). The sag can be calculated as a catenary or its parabolic approximation, as given in Eq.10.

mgL 1 H 2 mgL cosh mg H 2                 (10)
To calculate the tension, it is necessary to consider the thermal-tensional equilibrium of the conductor, as shown in Eq.11, where E represents the Young"s modulus of the conductor (Pa), A represents the cross-sectional area of the conductor (m 2 ) and β represents the conductor"s thermal expansion coefficient (K -1 ).

 

2 2 2 2 2 2 2 1 2 1 2 2 2 1 1 , c 2 , c H H 24 EA L g m H H 24 EA L g m T T EA                       (11)
For calculating the conductor operating temperature at a given current, or the maximum current for a given operating temperature, it is necessary to solve the energy balance between the heat dissipated in the conductor by the current, and the thermal exchange on its surface, as given in Eq.12

E 2 s r c R I q q q    ( 12
)
where q c represents convective heat exchange (Wm -1 ), q r represents radiative heat exchange (Wm -1 ), q s represents solar radiation (Wm -1 ) and I 2 R E represents the heat dissipated in the conductor due to the Joule effect (Wm -1 ). The proposed formulae [START_REF] Iec | IEC 60826: Loading and strength of overhead transmission lines[END_REF] were used for the calculation of the contribution of solar radiation, radiative heat exchange and convective heat exchange as given in Eq.13-15 respectively,

Sr D q s     (13)   D T T q 4 a 4 c SB r          (14)   a c c T T Nu q        ( 15
)
where α represents the absorption coefficient, D represents the external diameter of the conductor (m) and Sr represents solar radiation (Wm -2 ), ε represents the emission coefficient, ζ SB represents the Stefan-Boltzmann constant (Wm -2 K -4 ), and T c and T a represent the respective conductor and ambient temperatures (K), Nu represents the Nusselt number and λ represents the air thermal conductivity (Wm -1 K -1 ).

The influences of wind direction and natural convection on convective heat exchange are not considered in the IEC standard model [START_REF] Iec | IEC 60826: Loading and strength of overhead transmission lines[END_REF]. However, in this research these effects were considered to be important, particularly as a wind direction perpendicular to the conductor would maximise the turbulence around the conductor and hence the heat exchange on its surface whereas a wind direction parallel to the conductor would reduce the heat exchange with respect to perpendicular wind direction. Therefore the modifications (WG22. [START_REF]The Thermal Behaviour of Overhead Conductors -Section 1[END_REF]1992) given in Eq.16 and Eq.19 were used. It is possible to calculate the Nusselt number, Nu, from the Reynolds number, Re, as shown in Eq.17.

The Reynolds number can be calculated using Eq.18

  3 , dir K 2 , dir 1 , dir dir Wd sin K K K    (16)   61 . 0 2 . 0 dir Re 23 . 0 Re 65 . 0 K Nu      (17) 78 . 1 a c 9 2 T T D Ws 10 644 . 1 Re              (18)
where K dir represents the wind direction influence coefficient, K dir,1-3 represent wind direction coefficient constants, Wd represents the wind-conductor angle (rad) and Ws represents the wind speed (ms -1 ).

For null wind speeds, the Nusselt number must be calculated as in Eq.19 where Gr is the Grashof number, calculated as in Eq.20, and Pr is the Prandtl number

  2 , nat K 1 , nat Pr Gr K Nu    (19)     2 a c a c 3 2 T T g T T D Gr        ( 20
)
where K nat,1-2 represent natural convection coefficients and υ represents kinematic viscosity (m 2 s -1 ). It should be noted that for wind speeds between 0-0.5ms -1 the larger of the Nusselt numbers resulting from Eq.17 and Eq.19 should be used.

Electric cables

The current carrying capacity (ampacity) of electric cables is limited by the maximum operating temperature of the insulation. Sustained high currents may generate temperatures in exceedance of the maximum operating temperature, causing irreversible damage to the cable. In extreme cases this may result in complete insulation deterioration and cable destruction. The conductor temperature in steady-state conditions was modelled [START_REF] Neher | The calculation of the temperature rise and load capability of cable systems[END_REF][START_REF] Iec | IEC 60826: Loading and strength of overhead transmission lines[END_REF]IEEE, 1994) to account for the heat balance between the power dissipated in the conductor by the Joule effect, and the heat dissipated in the environment through the thermal resistance (R T ) of the insulation and the soil, due to the temperature difference ΔT as shown in Eq.21. The electrical current rating, I (A), may then be calculated, as shown in Eq.22.

T E 2 R T R I   (21) T E R R T I    ( 22
)
Refinements incorporating dielectric losses, q d (Wm -1 ), the number of conductors in the cable, n, eddy currents and circulating currents in metallic sheaths, (λ 1,2 ), electrical resistance variation with conductor temperature, R E (T c ) (Ω), skin and proximity effects and the thermal resistance of each insulating layer (R T,1-4 ) lead to the more complex expression for calculating the cable ampacity as given in Eq.23.

 

)] R R )( 1 ( n R ) 1 ( n R [ T R )] R R R ( n R 2 1 [ q T I 4 , T 3 , T 2 1 2 , T 1 1 , T c E 4 , T 3 , T 2 , T 1 , T d                (23)
Thermal resistances (R T,1-3 ) for cylindrical layers are calculated using Eq.24 and soil thermal resistance (R T,4 ) is modelled using Eq.25

             d d D 2 1 ln 2 R T s 3 , 2 , 1 T (24)                        1 D z 2 D z 2 ln 2 R 2 b b T s 4 T (25)
where ρ s-T represents the soil thermal resistivity (Wm -1 K -1 ), D and d represent the respective external and internal diameters (m) for the calculated layer and z b represents the burial depth of the cable (m).

Other calculation methods [START_REF] Iec | IEC 60826: Loading and strength of overhead transmission lines[END_REF] have to be utilised when operating conditions differ from those stated above (for example when the cable is in a duct or in open air). The model described above requires detailed knowledge of the electric cable installation. However, this information may not always be available and therefore it is difficult to make practical use of the model. In these circumstances an alternative model given in Eq.26 may be used (ENA, 2004). The rated current of electric cables, I 0 (A), is given in tables depending on the nominal voltage level, V (kV), the standardised cable cross-sectional area, A (m 2 ) and laying conditions (trefoil, flat formation; in air, in ducts or directly buried). The dependence of the cable ampacity on the actual soil temperature, T s (K) away from the rated soil temperature T s rated (K) as well as the actual soil thermal resistivity away from the rated soil thermal resistivity is made linear through the coefficients ξ T and ξ ρ respectively.

          rated T , s T , s rated s s T 0 T T laying , V , A I I             (26)
Since this research concerns the influence of environmental conditions on component ratings, the effect of the nominal voltage level which influences the dielectric loss, q d in Eq.23 is not considered. The effect of the heating given by adjacent components is also neglected as it is assumed that each cable has already been de-rated to take this effect into account.

Power transformers

The thermal model (IEC, 2008) can be used to calculate the winding hot spot temperature for power transformers. This is the most important parameter since hotspot temperature exceedance can damage the transformer in two ways. Firstly, a temperature exceedance of 120ºC-140ºC can induce the formation of bubbles in the coolant oil, which in turn is liable to cause an insulation breakdown due to the local reduction of dielectric insulation strength. Secondly, high temperatures increase the ageing rate of the winding insulation. For this reason the maximum operating temperature should not exceed the rated value. The thermal model consists of a heat balance between the power dissipated in the winding and iron core, and the heat transferred to the environment via the refrigerating circuit. Considering the ambient temperature, T a (K), thermal resistance between the windings and the oil, R T,W (mKW -1 ), the thermal resistance between the heat exchanger and the air, R T,HE (mKW -1 ) and the power dissipated into the core, I 2 R E, windings (W), it is possible to calculate the hot spot temperature, T HS (K) as in Eq.27.

  HE

, T W , T windings , E 2 a HS R R R I T T     (27)
Eq.27 is discussed [START_REF] Susa | Dynamic thermal modelling of power transformers[END_REF] and leads to the IEC standard model for rating oil-filled power transformers as shown in Eq.28

    y TO HS x 2 a TO a HS K T T r 1 rK 1 T T T T                ( 28
)
where T TO represents the top oil temperature (K), r represents the ratio between winding and core losses, K represents the loading ratio of the transformer, x represents the transformer oil exponent and y represents the transformer winding exponent.

The maximum rating can be obtained by iteration, once the hot spot temperature has been set, and tabulated values for the parameters are given (IEC, 2008) for transformers with different types cooling system. Correction factors (IEC, 2008) can be used to model other operating conditions such as transformers operating within enclosures. Transformer cooling systems are classified with an acronym summarising (i) the coolant fluid: oil (O) or air (A), (ii) the convection around the core: natural (N), forced (F) or direct (D), (iii) the external refrigerating fluid: air (A) or water (W) and (iv) the external convection method: natural (N) or forced (F). Typically distribution transformers have ONAN or ONAF cooling systems.

Thermal state estimation

This section describes the approach adopted to estimate, correct and interpolate environmental conditions to represent more accurately the actual environmental operating conditions for areas of the distribution network. This may then be used together with the models described in Section 4 to provide an estimation of power system component real-time thermal ratings.

Environmental condition estimations

The inverse distance interpolation technique [START_REF] Shepard | A two-dimensional interpolation function for irregularly-spaced data[END_REF]) allows environmental conditions to be determined over a wide geographical area using a reduced set of inputs. This is attractive for situations where a large amount of installed measurements may be financially unattractive to the DNO or generator developer. The technique is also computationally efficient and allows the input locations to be readily adapted. The wind speed correction process is described below, as is the soil parameter correction process. Wind direction, air temperature and solar radiation values are included within interpolations but do not require the application of a correction factor. At each point in the geographical area, κ, the value of the parameter, Z, representing the environmental condition can be estimated as a weighted average of the parameter values known at ι points. The weighting factor is a function of the distance between the points as shown in Eq.29.

           2 , 2 , d 1 d 1 Z Z (29)
Ground roughness influences wind speed profiles and may lead to differences between the wind speed recorded by anemometers and the actual wind speed passing across an overhead line, particularly if the anemometer and overhead line are installed at different heights. This may be corrected using the wind profile power law given in Eq.30. The wind speed at two different heights is linked with the ground roughness through the exponent Kshear. Values of Kshear for different ground types are given in [START_REF] Iec | IEC 60826: Loading and strength of overhead transmission lines[END_REF].

c a Kshear ref c Kshear a ref a z z z z Ws Ws                    (30)
Using Eq.30, the anemometer wind speed, Ws a (ms -1 ) at the weather station height, z a (m) is extrapolated to a reference height, z ref (m), to remove ground roughness dependence represented by the parameter Kshear a . The values from different anemometer locations may then be interpolated, using Eq.29 to provide a wind speed estimate at the reference height for a particular geographical location. The ground roughness at this location is then taken into account through the coefficient Kshear c along with the conductor height, z c (m) in Eq.30 to estimate the wind speed, Ws (ms -1 ) across the overhead line.

Electric cable ratings are dependent on soil temperature and soil thermal resistivity, as well as cable construction, burial layout and burial depth (which is typically 0.8-1m). UK MetOffice datasets contain information regarding soil temperatures at a depth of 0.3m. However, no information is currently available from this source regarding soil thermal resistivity. Depth-dependent soil temperature distributions may be calculated using the Fourier law [START_REF] Nairen | Heat transfer in ground heat exchangers with groundwater advection[END_REF] as shown in Eq.31

            dz dT dz d dt dT s T s s (31)
where T s represents the soil temperature (K), t represents time (s), z represents soil depth (m) and δ s- T (θ) represents the soil thermal diffusivity (m 2 s -1 ) as a function of gravimetric water content. Boundary conditions are set up for the lower soil layer (for example a constant temperature of 10 o C at a depth of 2m) and soil temperature readings are used for the upper layer. Soil thermal resistivity ρ s-T (mKW -1 ) may be calculated from Eq.32 using the soil thermal diffusivity, δ s-T (m 2 s -1 ), the dry soil density ρ s-density (kgm -3 ), and the soil thermal capacitance, C s-T (Jkg -1 K -1 ).

  1 T s density s T s T s C            ( 32
)
Soil thermal diffusivity and soil thermal capacity are influenced by soil composition, N (%), and soil gravimetric water content, θ and can be calculated using Eq. 33-34 (Nidal, 2003).

            79 . N 209 . 0 8 . 14 T s (33)             81 . 5 753 . 0 N 00561 . 0 224 . 0 C density s T s (34)
Ground water content may be determined using the closed form of Richard"s equation [START_REF] Celia | A general mass-conservative numerical solution for the unsaturated flow problem[END_REF] as described in Eq.35 after the calculation of the unsaturated hydraulic diffusivity, δ s-θ (θ) (m 2 s - 1 ) and the unsaturated hydraulic conductivity, k s-θ (θ) (ms -1 ) [START_REF] Van Genuchten | A closed for equation for predicting the hydraulic conductivity of unsaturated soils[END_REF].

                     s s k dz d dz d dt d (35)
In order to solve Eq.35, boundary and initial conditions must be specified. A constant water content equal to the saturation value can be set at a depth of 2.5 metres, corresponding to the UK water table. Furthermore, the ground-level water content can be linked to MetOffice rainfall values, l r (mm) using the model described in Eq.36, where Krain 1 represents the normalized soil water loss (day -1 ) and Krain 2 represents the normalized net rainfall coefficient (day -1 mm -1 ) (Rodriguez-Iturbe, 1996).

) t ( l Krain ) t ( Krain dt d r 2 1        (36)

Component rating estimations

In this section a description of the algorithm responsible for the state estimation is given. The primary aim of the thermal state estimation algorithm is to allow the rating of components, which are not directly monitored within the power system, to be assessed. Thermal state estimations facilitate the precise and reliable assessment of environmental conditions whereby a minimal amount of meteorological monitoring installations facilitate the assessment of component thermal ratings within a wide area. This may then be validated through the carefully selected monitoring of component operating temperatures. The algorithm provides a reliable estimation of power system component thermal ratings described by an appropriate cumulative probability function. A state estimation technique based on the Montecarlo method is used, giving a more complete description of the possible states of the system. The minimum, maximum, average and standard deviation of component rating forecasts may be calculated according to the possible forecasted weather conditions. As necessary for overhead lines and electric cables, each component is divided into sections to take into account different thermal operating conditions such as overhead line orientations and changes in electric cable installation conditions. The section resulting in the lowest rating values is then used to provide a rating for the entire component. Furthermore, the deployment of a real-time thermal rating system underpinned by thermal state estimation techniques has the potential to reduce the necessity of auxiliary communications infrastructure whilst simultaneously increasing the reliability of the system if measurement or communication failures occur. The thermal state estimation algorithm is illustrated in Fig. 2, where it is possible to see the following steps:

1. Forecasted weather data is read from an external source (in this case the database "a"), where real-time readings from different meteorological station locations within the distribution network are stored. 2. A set of values for weather parameters is calculated in the following way: From the data read in "1" the parameters of a cumulative probability function are calculated. In this case the Beta probability function is used. A random value for the probability is selected and from the cumulative probability function the corresponding parameter value is found. This is repeated for each weather parameter. 3. For each section of the component the values of local environmental condition are calculated with the methodology described in Section 5.1, then the rating is calculated using the models described in Section 4. The result is stored temporarily in "b". 4. The component rating is calculated by selecting the minimum rating of each section. The results are temporarily stored in "c". 5. The steps from 2 to 4 are repeated for a fixed number of times γ. 6. The precision of the result is compared with a predefined value. If the result is not acceptable, a new value for γ is calculated and the steps from 2 to 5 are repeated 7. Component ratings stored in "c" are analysed in order to calculate the minimum, maximum, average value and standard deviation for each component of the network.

Generator output control strategies

This section describes strategies for the power output control of single and multiple generation installations.

Single generation installations

A number of different solutions are presented (ENA, 2003) that may be developed to manage, in an active manner, the power flows associated with the connection of a single generation installation.

The most basic systems involve the disconnection of generation in the event that network power flow congestion occurs when utilising component static thermal ratings. This solution may be developed further by actively switching between seasonal ratings based on fixed meteorological assumptions and adjusting the number of disconnected generation units accordingly. More sophisticated solutions are developed from the principle of generator 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 generator is controlled to ensure the capability of the network is not exceeded. This may be developed as a solution utilising component static ratings, with demandfollowing control of the generator output, or as a solution utilising component real-time thermal ratings, with demand-following control of the generator output. Although generators may, at times, be requested to operate in voltage control mode, it is in the interest of the operators to maximise annual exported active energy (MWh) as that directly relates to the revenue of the generation installation owner. Thus it is assumed that the control system outlined in this chapter is utilised for generators exporting real power at unity power factor. An assessment of the real power output adjustment required by a generator may be calculated in Eq.37-38, relating to the component power flow sensitivity factor and the real-time thermal rating of the component

  m , P k , i k , i m , P dG dP P G    (37)                           2 k , i 2 k , i 2 k , i 2 lim Tar k , i Q ' S ' Q ' ' S U P (38)
where ΔG P,m (MW) is the required change in real power output of the generator at node m, ΔP i,k (MW) is the required change in real power flow through the component, from node i to node k in order to bring the resultant power flow back within thermal limits, dP i,k /dG P,m is the power flow sensitivity factor that relates the change in nodal real power injection at m with the change in real power flow seen in the component from node i to node k, "S i,k (MVA) is the apparent power flow in the component before control actions are implemented, "Q i,k (MVAr) is the reactive power flow in the component from node i to node k before the control actions are implemented, U Tar is the target utilisation limit of the component after the control actions have been implemented, S lim (MVA) is the static or real-time thermal rating of the component and "Q i,k (MVAr) is the reactive power flow in the component from node i to node k after the control actions have been implemented. Rather than neglecting MVAr flow, it may be assumed constant for a particular operating condition. Thus dP i,k /dG P,m >> dQ i,k /dG P,m and a simplification can be made to Eq.38 that "Q i,k ≈ "Q i,k .

Multiple generation installations

Present last-in first-off control strategies include the complete disconnection of generation installations or the power output reduction of generators in discrete intervals [START_REF] Roberts | Network Management Systems for Active Distribution Networks -A Feasibility Study[END_REF]. A step beyond this could be to implement a last-in first-off "sensitivity-based" control strategy whereby generator power outputs are adjusted (in contractual order) based on Eq.37-38. Moreover, these equations may be developed further to define the power output adjustment of multiple generation installations (assuming contracts are in place to allow generator operation outside of a last-in first-off constraint priority) in order to maximise the annual energy yield of the aggregated generation installations. Two candidate strategies are outlined below:

1. Equal percentage reduction of generator power output

The equal percentage reduction signal, Φ, broadcast to multiple generators in order to proportionally reduce their power output (taking power flow sensitivity factors into account) may be assessed as in Eq.39-40.

    m , P m , P G G (39)             m 0 m m , P ik m , P ik dG dP G P (40)
This strategy may be seen as the most "fair" option since each generator is constrained as an equal proportion of their present power output.

Most appropriate technical strategy

In order to minimise the overall generator constraint and thus maximise the annual energy yield of the aggregated generators, an assessment of the required generator power output adjustment is given in Eq.41. In this case, rather than adjusting generators in contractual order according to the historical order of connection agreements, the generator constraint order is prioritised according to the magnitude of the power flow sensitivity factors. Thus the generator with the greatest technical ability to solve the power flow congestion is adjusted first.

Case study: Power output control of multiple generators informed by component thermal ratings

The following case study illustrates various aspects of the development of a power output control system for generation installations. An assessment of power flow sensitivity factors is made which allows thermal vulnerability factors to be calculated. This informs the development of a real-time thermal rating algorithm for an electric cable section within the case study network. An offline simulation is conducted which allows the energy yield of candidate strategies for the power output control of multiple generators (informed by static and real-time thermal ratings) to be assessed.

Case study network description

The case study network shown in Fig. 3 was derived from a section of the United Kingdom generic distribution system (UKGDS) "EHV3" network [START_REF] Foote | United Kingdom Generic Distribution System (UKGDS) -Summary of EHV Networks[END_REF]. A hydro generator and two combined heat and power (CHP) generators were connected to the network at 33kV nodes. A summary of the generator properties is given in Table 1.

Generator

Grid supply point

Power flow constraint

Interconnector Fig. 3 United Kingdom generic distribution system case study

Thermal vulnerability factor assessment

The most thermally vulnerable components within the case study network were identified using a thermal vulnerability factor assessment as given in Fig. 4 (and detailed in Sections 3.1 and 3.2). From this assessment it may be seen that a generator real power injection at node 352 increases the thermal vulnerability of the components between nodes 352 and 353, and between nodes 353 and 357 but reduces the thermal vulnerability of the component between nodes 316 and 353. Moreover, a generator real power injection at node 353 increases the thermal vulnerability of the component between nodes 353 and 357 but reduces the thermal vulnerability of the component between nodes 316 and 353. Furthermore, a generator real power injection at node 354 increases the thermal vulnerability of the components between nodes 316 and 353, 354 and 316, and 353 and 357. However, this injection has little impact on the thermal vulnerability of the component between nodes 352 and 353. In this particular case, multiple generator injections cause the thermal vulnerability factors to accumulate for the electric cable between nodes 353 and 357, making it the most thermally vulnerable component. Having identified this component as being a potential thermal pinch-point and causing power flow congestion within the network, the targeted development of a real-time thermal rating system is informed.

Component real-time thermal rating assessment

A summary of the characteristics of the thermally vulnerable electric cable are given in Table 2. The "Valley" UK MetOffice dataset for the calendar year 2006 was used with the thermal state estimation technique described by Eq.31-36 (Section 5.1) to estimate environmental operating conditions local to the electric cable. This information was used to populate the electric cable steady-state thermal model described by Eq.26 (Section 4.2). The resulting real-time thermal rating for the electric cable, together with the static rating, is given in Fig. 5. It was found that the average uprating for the electric cable, based on minimum daily ratings was 6.0% across the year.

Generator injection node:

-

Electro-thermal simulation approach

This section describes the offline electro-thermal simulation that was used to quantify the individual and aggregated generator annual energy yields (as seen in Section 7.6) for the candidate multiple generator control strategies. UKGDS annual ½ hour loading and generation profiles were utilised for the simulation of network power flows and busbar voltages through a load flow software tool. The open-loop power output control system compared network power flows to component static and realtime thermal limits for each ½ hour interval within the simulated year. When power flow congestion was detected (signified in this case by an electric cable utilisation, U 353,357 > 1) the controller implemented the relevant generator power output control algorithm as described below. A validating load flow simulation was conducted as part of the control solution to ensure that the updated generator power outputs solved the power flow congestion and did not breach busbar voltage limits (set to ±6% of nominal). The individual and aggregated generator power outputs were integrated across the simulated year in order to calculate the potential annual energy yields resulting from the candidate multiple generator control strategies, informed by component static and real-time thermal limits. The results are summarised in Section 7.6.

Last-in first-off discrete interval generator output reduction

In order to solve the power flow congestion, the power output of the generators was reduced in discrete intervals of 33% according to the contractual priority order given in Table 1. Thus the power output of the generator at node 354 was reduced by 33% then 66% followed by generator disconnection and the 33% power output reduction of the generator at node 352.

2. Last-in first off sensitivity-based generator output reduction Using Eq.38 with U Tar = 0.99, the required real power reduction in the electric cable, ΔP 353,357 in order to bring the resultant power flow back within the desired thermal limit was calculated. The required reduction in the power output of the first generator (contractually) to be constrained, ΔG P,354 , was calculated using Eq.42 for G P,354 -ΔG P,354 >0 and the output from G P,353 and G P,352 was unconstrained.

On occasions when the required constraint of the generator at node 354 would result in G P,354 -ΔG P,354 <0, the generator was disconnected and the remaining power flow congestion was solved by the next generator (at node 352) to be contractually constrained. Using Eq.38 with U Tar = 0.99, the required real power reduction in the electric cable, ΔP 353,357 in order to bring the resultant power flow back within the desired thermal limit was calculated. The equal percentage reduction signal, Φ, broadcast to all generators in order to solve the power flow congestion was calculated and implemented using Eq.43-46. Using Eq.38 with U Tar = 0.99, the required real power reduction in the electric cable, ΔP 353,357 in order to bring the resultant power flow back within the desired thermal limit was calculated. Since, in this case, G p,353 has the greatest technical ability to solve power flow congestion (as assessed by the comparative magnitude of power flow sensitivity factors in Table 1), Eq.47 was implemented to minimise the overall generation constraint and thus maximise the annual energy yield of the aggregated generators. On occasions when the required constraint of the generator at node 353 would result in G P,353 -ΔG P,353 <0, the generator was disconnected and the remaining power flow congestion was solved by the generator with the next-greatest power flow sensitivity factor (in this case at node 352).

Findings

The marginal annual energy yields resulting from the applied candidate control strategies informed by the electric cable real-time thermal ratings are summarised in Table 3. A similar table is given (Jupe & Taylor, 2009a) for the marginal energy yields resulting from static component ratings. The last-in first-off "discrete interval" strategy with static component ratings was used as a datum with annual energy yields of 106.38GWh,298.26GWh,120.84 GWh and 525.48GWh

Discussion

Considering Table 3, for the case study network it can be seen that the adoption of both real-time thermal rating systems and sensitivity factor-based generator power output control strategies have the potential to unlock gains in the aggregated annual energy yield contribution of multiple generation installations. Moving from a static rating system to a real-time rating system has the potential to unlock an extra 6.3% aggregated annual energy yield for the last-in first-off "discrete interval" power output reduction strategy. If a last-in first-off contractual priority is retained but a sensitivity-based power output control strategy is adopted this has the potential to unlock an extra 3.7% marginal aggregated annual energy yield. Moreover, a further 3.1% annual energy yield gain may be achieved by utilising the "most appropriate technical" strategy that minimises overall generator constraint. For the "equal percentage reduction of generator power output" strategy, it can be seen that the relative annual energy yields of G P,352 and G P,353 are reduced in order to achieve an aggregated generator annual energy yield gain. This phenomenon is even more pronounced in the "most appropriate technical" strategy where annual energy yield gains of 1.7% and 85.1% for G P,352 and G P,354 respectively are facilitated by the 12.0% reduction in the annual energy yield of G P,353 .

The aim of this research was to outline the developmental stages required in the output control of generation installations for improved new and renewable energy yield penetrations. The purpose of the thermal vulnerability factor assessment was to identify thermally vulnerable components within distribution networks. It has been shown that the thermal vulnerability factor assessment is appropriate for use in a strategic way to assess longer term and wide-spread generation growth scenarios. Therefore the thermal vulnerability factor assessment procedure would be valuable for DNOs looking to develop long-term generation accommodation strategies for areas of their network. Furthermore, the thermal vulnerability factor assessment identifies those components that would most benefit from being thermally monitored to unlock latent power flow capacity through a real-time thermal rating system. The derived power flow sensitivity factors are network configuration-specific and assume that the network configuration will not be frequently changing. It is feasible, however, to develop an online control system that makes use of alternative sets of the above mentioned predetermined power flow sensitivity factors based on network switch status information. In order to deploy the control strategies presented there clearly needs to be a contractual mechanism in place that gives an incentive to separately owned generators to have their power output reduced in order to maximise the overall annual energy yield. One such mechanism could be to set up cross-payments between generators whereby the generators that are constrained to maximise the aggregated energy yield are rewarded by payments from the other generators.

Conclusion

It is anticipated that distribution networks will continue to see a proliferation of generation from new and RE resources in coming years. In some cases this will result in power flow congestion with thermally vulnerable components restricting the installed capacity and energy yield penetration of generators. Therefore a system that can be developed for the management of power flows within distribution networks, through the power output control of generators, could be of great benefit. The development stages of such a system were outlined in Sections 3 -6. These included: (i) the identification of thermally vulnerable components through a thermal vulnerability factor assessment that related power flow sensitivity factors to component thermal limits, (ii) the targeted development of thermal models to allow latent capacity in the network assets to be unlocked through a real-time thermal rating system, (iii) the development of thermal state estimation techniques that would allow component steady-state thermal models to be populated with real-time data from meteorological monitoring equipment installed in the distribution network (iv) the use of the real-time thermal ratings together with power flow sensitivity factors to control the power output of multiple generators. The development stages were illustrated with a case study and the energy yields resulting from the offline application of strategies were quantified. It was demonstrated for the particular case study scenario that the adoption of both a real-time thermal rating system and power flow sensitivity factor-based control strategies have the potential to unlock gains in the aggregated annual energy yield of multiple generation installations. Work is continuing in this area to realise the potential of generator output control systems for increased new and renewable energy yield penetrations.

Fig. 2

 2 Fig. 2 Component rating estimation flow chart

Fig. 5

 5 Fig. 5 Electric cable static and real-time thermal rating

  percentage reduction of generator power outputs

Component (From node -to node) Thermal vulnerability factor (TVF)

	6	352	353	354
	5
	4
	3
	2
	1
	0
	-1	316 -353	354 -316	352 -353	353 -357
	-2
	-3
	4
	Fig. 4 Thermally vulnerable component assessment for UKGDS case study
	Electric cable characteristic		Value / category
	Resistance, R E (pu)		0.01489
	Reactance, X (pu)		0.01296
	Cross-sectional area, A (mm 2 )		150
	Standard thermal rating, S lim (MVA)	30
	Nominal line-to-line voltage, V (kV)	33
	Standardised phase current rating, I 0 (A)	525
	Installation conditions		Flat formation
				Buried in a duct
	Table 2. Electric cable characteristics

Table 3 .

 3 for G P, 352 , G P, 353 , G P, 354 and G P, aggregated respectively. Generation marginal annual energy yields

		Generation marginal annual energy yield (%)
	Generator control strategy	with electric cable real-time thermal rating
		G P, 352	G P, 353	G P, 354	G P, aggregated
	Last-in first-off (Discrete-interval)	1.7	0.0	25.8	6.3
	Last-in first-off (Sensitivity-based)	1.7	0.0	41.9	10.0
	Equal % reduction of power output	-5.5	-6.2	73.6	12.3
	Most appropriate technical strategy	1.7	-12.0	85.1	13.1


P C Taylor

C E Jupe

F Lyons

Andrea Michiorri

P Trichakis

Active Management of MV and LV Distribution Networks

Keywords: Distributed Generation, Micro-generation, Distribution Networks, Active Network Management, Active Distribution Automation

whether they are published or not. The documents may come

Introduction

Distributed generation (DG) includes all electricity generation plants connected to distribution networks. These may employ a number of different technologies and fuels using synchronous or nonsynchronous electrical generators. Most distributed generators are powered either by renewable energy sources such as wind, solar and hydro, or by combined heat and power (CHP) units using conventional fuels with higher overall energy efficiency than electricity only cycles. These generating plants are often connected to passive distribution networks at 33kV and below. Furthermore, in addition to the DG embedded in Medium Voltage (MV) distribution networks, micro-generators installed at end users at Low Voltage (LV) levels are gaining significant momentum, due to potential advantages regarding reliability, energy efficiency and power quality.

For the United Kingdom (UK), Governmental targets for renewable and CHP generation by 2010 are: (i) to produce 10% of the UK's energy needs from renewable energy sources; and (ii) to reach a total of 10GW of CHP generating capacity [START_REF]Energy white paper: our energy futurecreating a low carbon economy[END_REF]. At the same time, installed micro-generation capacity could grow to as much as 8GW by 2015 according to a major recent study [START_REF] Mcdonald | System integration of additional micro-generation costs and benefitsfinal draft report[END_REF], while a second study has suggested that micro-generation technologies could supply between 30 to 40 per cent of the UK's electricity demands by 2050 [START_REF]Potential for microgeneration -study and analysis[END_REF].

This anticipated growth in DG will entail the introduction of large numbers of generators of different types and sizes at various locations and voltage levels of the electricity distribution network. However, these have often been designed and operated based on planned centralized generation, and on the assumption that the current always flows from the substations to the ends of feeders. Hence, a key challenge facing the electricity industry is to accommodate the anticipated growth of DG onto existing distribution networks without compromising security of supply, while at the same time minimizing costs incurred to customers and maximizing the potential benefits gained from these units. There is a consensus within industry and academia that the most appropriate way of achieving this is by active control techniques [START_REF]Embedded generation on actively managed distribution networks[END_REF]. In response to this, the European Commission have developed the concept of SmartGrids [START_REF] Smartgrids | [END_REF], which aims to formulate and promote a vision for the development of European electricity networks looking towards 2020 and beyond.

This paper presents research currently carried out at Durham University for the development and evaluation of active network management techniques at the MV and LV levels of distribution networks with high levels of DG and micro-generation. The first project describes the development of an active thermal controller for distribution networks at an MV level. The research is being conducted by a consortium including Parsons Brinckerhoff, Durham University, Scottish Power Energy Networks (SPEN), AREVA T&D and Imass, part-sponsored by the UK Government's Technology Strategy Board (TSB). The architecture of the controller is outlined that will provide DG set points to the network operator, informed by the real time dynamic thermal rating (DTR) of network components. This moves beyond current operational regimes that, at times, assume a fixed set of worst-case meteorological conditions, to create capacity headroom that could allow greater DG access to the network.

Regarding the research and development of DTR control systems, relevant work is described in [START_REF] Douglass | Real-time monitoring and dynamic thermal rating of power transmission circuits[END_REF] and [START_REF] Nuijten | Future network planning and grid control[END_REF], considering overhead lines, underground cables and power transformers. Research, pertaining to the dynamic rating of overhead lines, has been carried out by different network operators, such as Iberdrola in [START_REF] Soto | Increasing the capacity of overhead lines in the 400 kV Spanish transmission network: real time thermal ratings[END_REF], NIE Energy in [9] and by Central Networks, E.ON-UK in [START_REF] Yip | Dynamic line rating protection for wind farm connections[END_REF]. The possible advantages of a DTR system implementation 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].

The second project is based on work funded by Engineering and Physical Sciences Research Council (EPSRC) and E.ON-UK Power Technologies. The project investigates the development and evaluation of a distributed control approach, based on Multi Agent Systems (MAS) technology, for MicroGrids. MicroGrids are public low voltage feeders containing a mixture of several micro-sources, distributed energy storage units (ESUs) and controllable loads, and appear to the upstream distribution network as a controllable entity [START_REF]More MicroGrids[END_REF]. Through intelligent co-ordination of micro-generators and ESUs, coupled with demand side management techniques, MicroGrids have the potential to offer significant improvements in the commercial value and environmental impact of the installed micro-generation. Moreover, it is possible that MicroGrids could be used to provide ancillary services to Distribution Network Operators (DNOs) such as local voltage control and spinning reserve.

A number of demonstration projects have been commissioned internationally with regards to MicroGrids, in particular in Europe, USA and Japan [START_REF] Barnes | Real world MicroGrids -an overview[END_REF][START_REF] Morozumi | Overview of Microgrid research and development activities in Japan[END_REF]. However, development of these projects has mostly been undertaken independently and significant differences exist in network topology, the rating and mixture of micro-generation, as well as on their operational aims and their specific controller implementations. One such project is currently under development in Durham University with three main aims: (i) to assess the impact of micro-generation on existing passive low voltage distribution networks, (ii) to validate distribution network models that have already been developed [START_REF] Lyons | Small scale energy zones and high concentrations of small scale embedded generation[END_REF][START_REF] Trichakis | An investigation of voltage unbalance in low voltage distribution networks with high levels of SSEG[END_REF], and (iii) to implement, test and refine control algorithms for future active distribution networks. Results from a practical implementation of an agent-based control system on the Experimental MicroGrid at Durham University are presented and discussed in [START_REF] Lyons | A practical implementation of a distributed control approach for MicroGrids[END_REF][START_REF] Trichakis | An agent based control approach for Microgrids[END_REF].

Active Control of DG based on a DTR system at an MV level

This section describes the development of an active controller for DG based on a DTR system at MV level. A service-oriented architecture (SOA) has been adopted that will be implemented using web services (as shown in Figure 1). Aspects of the system are connected together using a service bus approach that allows all services to communicate with each other using industrial standard protocols. This approach gives complete flexibility for deployment of services over a distributed network, even through in practice the core services for the thermal controller are likely to be implemented on a single hardware platform. Thermal State Estimation (TSE): Thermal State Estimation is a fundamental part of the active thermal control system that this project aims to realise. The role of the TSE service is to manage the EPP results and the component thermal models to improve the estimation precision and to increase the controller's reliability. The method is described graphically in Figure 2. Starting with a probability distribution for each meteorological measurement, a corresponding probability distribution is calculated for each external parameter value and each component thermal rating. These estimates act as an input to the DTR algorithms and are used to calculate the rating and associated probability for each of the relevant network components. Network Management System (NMS): It is expected that electrical inputs for the thermal controller will be provided by the NMS. From the status of circuit breakers and switches in the network, faults and network reconfiguration events can be detected. A number of predetermined network operating states will be incorporated into the control system to allow network reconfigurations to be accommodated. Other electrical measurements such as generator outputs, network loads and voltages will allow an electrical network model to be populated in order to fully simulate the present network state. Optimisation: Using the NMS component power flows and building in any measurement uncertainty, the DTR and actual power flow will be compared for each component. The optimisation algorithms will aim to minimise the constraints placed on the generating schemes based on generation energy yield and economic considerations such as contractual obligations within operational constraints. Online Simulation Tool: Inputs from the NMS will be used in the online simulation tool to validate the generation set point(s) proposed by the optimisation service and ensure that the power flows across the network are managed effectively. The main purpose of the online simulation service is to ensure that a solution to a thermal event on the network will not result in the violation of operational voltage limits. Connection Manager: The final stage of the active thermal control system response is to dispatch a set of suggested P and Q set points to the various generation schemes within the jurisdiction of the thermal control system. This will be done by either closed-loop automatic active generation control or by advising controllers in the DNO control rooms.

Active management of LV distribution networks

The use of a distributed control approach for MicroGrids has been proposed in [START_REF] Kundur | Power system stability and control[END_REF]. This approach is realised through the development of a Multi Agent System (MAS) based control system. The proposed MAS-based control system is based on the idea of using agents to distribute the management and control tasks of the MicroGrid. Each agent makes control decisions autonomously and pro-actively in order to accomplish its design goals, but can also interact and cooperate with other agents.

Three types of agents have been identified in order to satisfy the specific control requirements of a MicroGrid and adhere to the specifications developed by the Foundation of Intelligent Physical Agents (FIPA): (i) direct control agents, (ii) indirect control agents and (iii) utility agents.

 1. Direct control agents (Generator Agent; Consumer Demand Agent; and Energy Storage Agent) use local measurements to directly control a power system entity (micro-generator, energy storage unit or demand) within the MicroGrid  2. Indirect control agents (Operational Goals Agent; Unbalance Agent; Voltage Regulation Agent; and Thermal Limits Agent) use global measurements to indirectly control one or more power system entities within the MicroGrid  3. Utility agents (Agent Management System; and Directory Facilitator) perform administrative duties and are in alignment with FIPA specifications.

Software Implementation

A software implementation of a MAS-based control system containing all system agents that have been identified in Section 4 is currently under development at Durham University. The MAS is written in JADE (Java Agent Development Framework), which is middleware implemented in Java for the development of fully FIPA-compliant MAS. The MAS is coupled with a relational database management system developed in MySQL. This is then linked to the power system simulation package PSCAD/EMTDC™ in order to evaluate the MAS based on its suitability to satisfy the specific control requirements of MicroGrids. Three different LV distribution networks will be considered as a MicroGrid: one urban generic UK LV distribution network described in [START_REF] Lyons | Small scale energy zones and high concentrations of small scale embedded generation[END_REF][START_REF] Trichakis | An investigation of voltage unbalance in low voltage distribution networks with high levels of SSEG[END_REF], one sub-urban generic European LV distribution network and one existing public UK LV distribution network operated by Central Networks. System agents have two functions: (i) reasoning and (ii) communications. They are responsible for interpreting the measurement data and deciding on the active (P) and reactive power (Q) set points of micro-generators, ESUs and controllable loads within the MicroGrid. Control decisions taken by the agents are stored in the MAS Outputs Database, which is also accessed through JDBC, and are then passed to the PSCAD™ simulation models. This allows control decisions taken by the JADE-based MAS to be evaluated on a power systems simulation package.

Based on general power system operation theory [START_REF] Kundur | Power system stability and control[END_REF], three operating states for system agents are implemented inside the functionality of the MicroGrid controller: (i) normal, (ii) alert and (iii) emergency state.  1. Normal State: If all system agents are operating in their normal state, all measured parameters (voltages, currents, power flows) are located within their desired operating limits. The aim of the controller is to maximise the total active power output of connected micro-generators and also to minimise system losses in the MicroGrid branches and the MV/LV windings of the distribution transformer supplying the MicroGrid. Loads and ESUs are not controlled during the normal state, because their operation is only considered for cases where they can contribute to secure system operation, or in order to meet an operational goal.  2. Alert State: If one or more of the measured system parameters exceeds their desired operating limits, the system enters an alert state where the goal is to restore all system agents back to their normal state. This is achieved either through energy storage and demand side management, or through reactive power support. This is done in order to avoid real power output curtailment of the connected micro-generators.

 3. Emergency State: The MicroGrid is said to be operating under an emergency state if one or more of the measured system parameters have exceeded their maximum or minimum permissible operating limits, according to either statutory regulations or equipment ratings. In this case, active power curtailment of the connected micro-generators is required in order to ensure secure system operation. The controller will be evaluated based on its suitability to satisfy the specific control requirements of MicroGrids, namely: (i) to overcome the LV distribution network constraints under investigation in the event of system disturbances; and (ii) to meet the MicroGrid's identified operational goals [START_REF] Trichakis | A distributed control approach for small scale energy zones[END_REF]. Results will be described in a forthcoming paper.

Laboratory-based Implementation

A laboratory based experimental MicroGrid is currently under development at Durham University [START_REF] Lyons | A practical implementation of a distributed control approach for MicroGrids[END_REF][START_REF] Trichakis | An agent based control approach for Microgrids[END_REF]. The experimental MicroGrid consists of one load emulator, one wind turbine generator emulator, one PV generation emulator, one dCHP emulator, one ESU emulator and one network connection emulator, as illustrated in Figure 3. Monitoring and control systems have been developed using the LabVIEW™ visual programming environment. This system enables the flexible, real-time measurement of system parameters (voltage, current, frequency etc) and also enables repeatable, controlled testing to be carried out. In addition, the LabVIEW™ based control system allows control of the individual micro-source emulators and ESU inverter interfaces in order to regulate active power flow. Reactive power flow absorbed and produced by the energy storage system may also be controlled. The experimental MicroGrid is therefore an enabler for the development and evaluation of centralised and distributed active network management techniques for low voltage distribution networks. Initial results of first-stage agent-based control systems developed in the LabVIEW™ visual programming environment are presented in [START_REF] Lyons | A practical implementation of a distributed control approach for MicroGrids[END_REF][START_REF] Trichakis | An agent based control approach for Microgrids[END_REF].

Conclusions

Interest in DG has increased greatly in recent years as fears over global warming, high oil prices, the decline of indigenous energy supplies and increasing government support are expected to lead to a continuous increase in electricity generated by renewable energy sources. This anticipated growth in DG is likely to present a number of technical, economic and regulatory challenges, as well as a number of potential economic, technical and environmental benefits. A key challenge facing the electricity industry is to accommodate the anticipated growth of DG onto existing distribution networks without compromising security of supply, while at the same time minimizing costs incurred to customers and maximizing the potential benefits gained from these units. There is a consensus within industry and academia that the most appropriate way of achieving this is by active control techniques.

This paper has presented current research at Durham University relating to the active management of distribution networks at MV and LV levels. A DTR system-based approach has been proposed for the control of DG at the MV level. Furthermore, a MAS-based distributed control approach has been presented for the coordinated control of micro-generators, energy storage units and controllable loads.

Figure 1 :

 1 Figure 1:Service-oriented architecture to be adopted for the thermal controller

Figure 2 :

 2 Figure 2: Thermal State Estimation process

Figure 3 :

 3 Figure 3: View of the experimental MicroGrid at Durham University


Andrea Michiorri
email: andrea.michiorri@dur.ac.ukp.c.taylor@dur.ac.uk

Philip C Taylor

FORECASTING REAL-TIME RATINGS FOR ELECTRICITY DISTRIBUTION NETWORKS USING WEATHER FORECAST DATA

whether they are published or not. The documents may come

INTRODUCTION

The rating of power system components is influenced by external parameters such as wind speed or air temperature, but the possibility of exploiting any increase in rating is problematic due to the variability of these external parameters. The technique referred to as "realtime ratings" involves real-time measurement of component temperatures and external parameters, such as air temperature or wind speed, in order to estimate component real-time ratings. Durham University is participating in a collaborative project with AREVA T&D, Imass, PB Power and ScottishPower EnergyNetworks, which aims to develop, install and test a power output control system for distributed generation informed by dynamic thermal ratings. Within this research, dynamic thermal ratings are defined as a timevariant 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 and it is assumed that there are no outages (planned or unplanned) present within the electrical power system. Previous research [START_REF] Roberts | Dynamic thermal rating for increasing network capacity and delaying network reinforcements[END_REF] has demonstrated the suitability of real-time ratings for distributed generation power output control. This paper describes research with a different approach: weather forecasts are used for producing rating forecasts for different time horizons. A number of perceived benefits are expected to be yielded by this approach: Firstly a reduction in the number of on-site weather stations would be possible since the necessary information could be gathered from meteorological offices. Secondly the availability of rating forecasts would enhance the decision making capability of distribution network operators regarding network power flow management. Decisions would be informed by both the instantaneous ratings as well as rating forecasts for different time horizons. Another innovation described in this paper is the utilization of an estimation technique, in order to assess the error associated with rating forecasts. The knowledge of the error associated with the use of state estimation techniques would potentially increase the distribution network operator's confidence in real-time rating systems. This paper is structured in the following way: Firstly a survey of related work is presented. Then the methodology used in the research is described and the data used for the simulations and the case study are presented and finally simulation results are given and conclusions are drawn.

RELATED WORK

This work aims to combine two different areas of research: power system component real-time ratings and forecast techniques. Research has been carried out on the two topics, but not on their combination. The concept behind real-time ratings is described in [START_REF] Roberts | Dynamic thermal rating for increasing network capacity and delaying network reinforcements[END_REF]. The description of an application of a real-time rating system for the transmission network in the region of Madrid is provided 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 and these estimations are used for calculating the real-time rating of an overhead line. The Electric Power Research Institute (EPRI) developed a similar system in the late 1990s considering overhead lines as well as other power system components. In [START_REF] Douglass | Real-time monitoring and dynamic thermal rating of power transmissioncircuits[END_REF] and [START_REF] Douglass | Fieldapplication 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. Forecast techniques have been applied to predict energy demand and wind power production. In [START_REF] Taylor | An evaluation of methods for very short-term load forecasting using minute-byminute British data[END_REF] different techniques for load demand forecasts such as ARIMA modelling, adapted exponential smoothing and weather forecasts were compared and it was found that the combination of weather forecasts and exponential smoothing provide a better approximation for load demand forecasts beyond one hour ahead. Another approach, described in [START_REF] Taylor | Using weather ensemble predictions in electricity demand forecasting[END_REF], bases the energy demand forecast on weather ensemble forecasts-a method which provides a probability distribution of the possible weather parameter values. This method is considered for a time horizon of up to 10 days. Regarding wind power forecasts, in [START_REF] Sanchez | Short-term prediction of wind energy production[END_REF] the system used in the on-line management of the Spanish transmission system is described. It makes use of several models and of adaptive estimation for the parameters. The final prediction is then obtained as a weighted average of the results of the different models. From the analysis of the research described above, it is possible to highlight the requirement for work in the area of components real-time rating forecasts. This paper aims to suggest a possible methodology for filling this gap.

METHODOLOGY

The research described in this paper adopts the following approach: Component thermal models available in literature are used for calculating component rating for particular weather conditions. A state estimation technique based on Montecarlo method is used for giving a more complete description of the possible states of the system, providing the minimum, maximum, average and standard deviation of the rating forecasts according to the possible forecasted weather conditions. Historical weather forecast data from the National Oceanic and Atmospheric Administration (NOAA) [8] is used as inputs to these models.

State estimation

In this section a description of the algorithm responsible for the state estimation is given. The aim of this algorithm is to provide a reliable estimation of circuit ratings described by an appropriate cumulative probability function. The circuit has been divided in several parts, for taking into account different soil roughness and line orientation. This makes it possible to calculate descriptors such as the minimum, maximum, average and standard deviation of the rating estimation. The algorithm developed is briefly illustrated in Figure 1, where it is possible to see the following steps: 1. Forecasted weather data is read from an external source (in this case the database "a"). This data, comprising the minimum, maximum, average and standard deviation of each parameter in the given period, is described in Section 3.3. 2. A set of values for weather parameters is calculated in the following way: From the data read in "1" the parameters of a cumulative probability function are calculated. In this case the Beta probability function is used. A random value for the probability is selected and from the cumulative probability function the corresponding parameter value is found. This is repeated for each weather parameter. 3. For each component of the circuit the rating is calculated using the models described in Section 3.

Overhead line rating

The fundamental idea behind component ratings is that the operating temperature limit of the 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 50 ºC and 90 ºC. 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 cable and the environment 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).

(1) Different methods have been suggested for the calculation of each one of these parameters. In this research the methodology previously described in [START_REF] Roberts | Dynamic thermal rating for increasing network capacity and delaying network reinforcements[END_REF]

Environmental condition interpolation

The inverse distance interpolation technique [START_REF] Shepard | Atwo-dimensional interpolation function for irregularly-spaceddata[END_REF] allows environmental conditions to be determined over a wide geographical area using a reduced set of inputs. In this case meteorological inputs are the weather forecasts from the NOAA at a height of 10m from the ground. Wind speed is corrected with the method described in Section 3.2.3. Wind direction, air temperature and solar radiation values were included within interpolations but did not require the application of a correction factor. At each point in the geographical area (k) the value of the parameter (Z) representing the environmental condition can be estimated as a weighted average of the parameter values known at i points. The weighting factor is a function of the distance between the points as shown in Equation ( 2).

ik ik i d i k d i Z Z    (2)

3.2.3

Wind speed correction Ground roughness influences wind speed profiles and may lead to differences between the wind speed estimated at a given height and location and the actual wind speed passing across an overhead line. This may be corrected using the wind profile power law given in Equation ( 3). The wind speed (w s ) at two different heights (z 1 and z 2 ) is linked with the ground roughness through the exponent K shear . Values of K shear for different ground types may be found in [START_REF]Loadingandstrengthofoverhead transmissionlines[END_REF].

    1 12 2 shear K ss z w z w z z     (3)
Using Equation (3) the forecasted wind speed is extrapolated to a reference height (in this case 100 meters) to remove ground roughness dependence. The values from different forecast locations may then be interpolated, using Equation (2), to provide a wind speed forecast estimate at the reference height for a particular geographical location. The ground roughness at this location is then taken into account and Equation ( 3) is used to estimate the wind speed across the overhead line.

Data

For the study described in this paper, weather forecasts from the NOAA [8] for the test area in Wales with a time step of 6 hours have been used. Data is described in Table 1. The Montecarlo simulation does not require simple parameter values, but a description of their probability, with the minimum, maximum, average and standard deviation. These values can be obtained with ensemble forecasts or time series analysis. In this study the precision of the forecast for different time horizons has been estimated from the precision of the forecast for each parameter in the period between 08-18/08/2008. The line passes through the two towns in an area characterised mainly by the presence of grass and inhabited areas. This is important since the different in ground roughness influences the value of wind speed as calculated in Equation (3). In Figure 2 a representation of the network studied is provided.

RESULTS AND DISCUSSIONS

The simulation results are shown in Table 3 and Figure 3, where the rating forecasts for the whole day are represented from the reference time of midnight. Two main considerations arise from the observation of these results: As expected, the error increases with the distance of the forecast from the reference time. At 6pm the possibility to have a real time rating below the value of the static seasonal rating is forecasted. In this case the minimum forecasted rating in the late afternoon corresponds with the daily peak for the power transfer on the line. If the line utilization increased because of an increased connection of distributed generation, this would create a problem and the necessity to curtail part of the generation. On the other hand, the ability to forecast this situation and to quantify its probability, would allow appropriate decisions for generation control to be taken. Considering the different precision found for different time horizons, it is recommended to take into account this parameter, along with the distance from the forecast reference time, when developing control strategies for power flow management.

CONCLUSION

In this paper a methodology for overhead line rating forecasts has been presented. This is based on the research regarding distribution network real-time rating estimations developed at Durham University. Weather forecasts were used with component thermal models and a state estimation technique based on the Montecarlo method in order to calculate a probability distribution for each circuit component's rating for different time horizons. The results have then been collated for calculating line rating probability distribution, and metrics such as minimum, maximum, average and standard deviation of the rating forecasts. The simulation highlights that the estimation precision tends to decrease with the distance from the reference time. It was also shown how a possible rating lower than the static seasonal rating was forecasted 18 hours ahead of the reference time. The work presented in this paper on rating forecasts is expected to enhance distribution network operator decision making capabilities regarding network power flow management. This is because decisions may be informed not only by instantaneous rating estimates, but also by the forecasted ratings for different time horizons. Work is continuing in this area to realise the potential of forecasted real-time ratings for electrical distribution networks.

2 . 1 .

 21 Figure 1: Montecarlo simulation basic flow chart 3.2 Models

Figure 2 :

 2 Figure 2: Network and site schematic representation

Figure 3 :

 3 Figure 3: Rating forecast

Table 1 weather forecast for 18/09/2008 on test area

 1

			Time horizon [h]
		0	6	12	18	24
	Ws [m/s]	1.8	2.5	1.7	1.2	3.4
	Wd [deg]	178	196	189	301	216
	Ta [°C]	10.3	9.8	16.1	12.9	12

Table 2 Forecast error minimum, maximum average and standard deviation

 2

				Time horizon [h]
		[%]	6	12	18	24
	Ws	min max	-28.30 13.79	-17.78 50.00	-30.19 27.27	-38.55 83.33
		st dev	14.24	21.60	18.17	38.96
	Wd	min max	-1.94 15.83	-3.33 1.67	-2.78 20.83	-8.33 23.61
		st dev	5.00	1.44	6.70	8.11
		min	-15.86	-9.93	-20.27	-16.22
	Ta	max	1.57	4.80	31.07	15.84
		st dev	6.07	5.78	12.06	8.51
	The network studied is part of the Manweb distribution
	network situated in an area attractive to prospective wind
	farm development. It is composed of a 132kV Lynx
	overhead line conductor with a maximum operating
	temperature of 50C connecting two towns 7 km apart.

Table 3 Rating forecast in MVA for different time horizons

 3

			Time horizon [h]
	[MVA]	0	6	12	18	24
	min		108	109	75	85
	max		142	134	141	152
	aver	138	133	122	98	130
	st dev		9	9	24	17

ACKNOWLEDGMENTS

The authors wish to acknowledge the Department for Innovation, Universities and Skills for funding, and the staff from AREVA T&D, Imass, PB Power and ScottishPower Energy Networks for their valuable input to this work.