Optimal power flow evaluation of distribution network capacity for the connection of distributed generation G.P. Harrison and A.R. Wallace Abstract: Distributed generation capacity will increase significantly as a result of UK Government- led targets and incentives. Whereas the technical problems arising from distribution-level connections may be mitigated for individual connections, the anticipated connection volumes imply a potential risk of conflict between connections, in that inappropriately sized or located plant could constrain further development of the network and consequently threaten the achievement of renewable energy targets. One means of addressing this risk is to encourage development at sites that are more suitable and at the same time discouraging those at inappropriate ones. First of all network operators must be able to evaluate the available capacity on the system (i.e. the headroom). A technique is presented that facilitates such an analysis. Termed ‘reverse load-ability’, the approach models fixed-power factor distributed generation as negative loads and uses the optimal power flow to perform negative load shedding that effectively maximises capacity and identifies available headroom. The technique is applied to an extensive distribution and sub- transmission network. It rapidly identifies available headroom within the imposed thermal and voltage constraints. Furthermore, its use is demonstrated in examining the consequences of a sequence of connections in terms of the impact on available headroom and in sterilising the network. MVA DG MVA capacity of the distributed generators (DGs) pu MVA 0 initial MVA capacity of DGs, pu MW 0 initial active power capacity of DGs, pu V bus voltage magnitude, pu V MIN minimum bus voltage V MAX maximum bus voltage S branch power flow, MVA S MAX branch thermal limit, MVA c capacity adjustment factor c MIN minimum capacity adjustment factor c MAX maximum capacity adjustment factor C capacity value, per unit megawatt n number of buses available for capacity addition i DG bus index j bus index k branch index 1 Introduction The European Union Renewables Directive and national incentives such as the UK Renewables Obligations [1, 2] are encouraging the development of renewable energy re- sources. These distributed generators (DGs) will increas- ingly be connected to distribution networks given that the resources are generally located in remote areas. The connection of DGs fundamentally alters distribution network operation and creates a variety of well known impacts that range from bi-directional power flows to increased fault levels although the capacity is predominantly limited by the voltage rise in more rural networks [3] .A range of options exist to mitigate adverse impacts, under existing (deep-connection) UK commercial arrangements but the developer will largely bear the financial responsi- bility for their implementation. The economic implications can make potential distributed generation schemes less attractive and has been an impediment to the development of renewable energy. Whereas the shallower connection charging regime to be introduced in April 2005 will lessen this effect, the burden will increasingly fall on the distribution network operators (DNOs) who will have to justify their investment and tariff setting approaches [4] .A further issue is that current DNO policies of assessing DG connections on a first-come first-served basis can limit holistic distributed generation development in that an early and perhaps quite minor connection can constrain devel- opment of other, potentially larger, opportunities in the same area, effectively ‘sterilising’ parts of the network. The need to mitigate DG-related effects occurs because of the mismatch between the location of renewable resources and the capability of the network in those areas to accept new generation. The problem is compounded since, currently, the connection process ignores the consequent effects of decisions. One means of tackling both challenges is for the DNOs to encourage development at the most The authors are with the School of Engineering and Electronics, Institute for Energy Systems, University of Edinburgh, King’s Buildings, Mayfield Road, Edinburgh EH9 3JL, UK r IEE, 2005 IEE Proceedings online no. 20041193 doi:10.1049/ip-gtd:20041193 Paper first received 4th September 2003 and in revised form 7th September 2004 IEE Proc.-Gener. Transm. Distrib., Vol. 152, No. 1, January 2005 115
Transcript
Optimal power flow evaluation of distributionnetwork capacity for the connection of distributedgeneration
G.P. Harrison and A.R. Wallace
Abstract: Distributed generation capacity will increase significantly as a result of UKGovernment-led targets and incentives. Whereas the technical problems arising from distribution-levelconnections may be mitigated for individual connections, the anticipated connection volumesimply a potential risk of conflict between connections, in that inappropriately sized or located plantcould constrain further development of the network and consequently threaten the achievement ofrenewable energy targets. One means of addressing this risk is to encourage development at sitesthat are more suitable and at the same time discouraging those at inappropriate ones. First of allnetwork operators must be able to evaluate the available capacity on the system (i.e. theheadroom). A technique is presented that facilitates such an analysis. Termed ‘reverse load-ability’,the approach models fixed-power factor distributed generation as negative loads and uses theoptimal power flow to perform negative load shedding that effectively maximises capacity andidentifies available headroom. The technique is applied to an extensive distribution and sub-transmission network. It rapidly identifies available headroom within the imposed thermal andvoltage constraints. Furthermore, its use is demonstrated in examining the consequences of asequence of connections in terms of the impact on available headroom and in sterilising thenetwork.
MVADG MVA capacity of the distributed generators(DGs) pu
MVA0 initial MVA capacity of DGs, puMW0 initial active power capacity of DGs, puV bus voltage magnitude, puVMIN minimum bus voltageVMAX maximum bus voltageS branch power flow, MVASMAX branch thermal limit, MVAc capacity adjustment factorcMIN minimum capacity adjustment factorcMAX maximum capacity adjustment factorC capacity value, per unit megawattn number of buses available for capacity
additioni DG bus indexj bus indexk branch index
1 Introduction
The European Union Renewables Directive and nationalincentives such as the UK Renewables Obligations [1, 2] are
encouraging the development of renewable energy re-sources. These distributed generators (DGs) will increas-ingly be connected to distribution networks given that theresources are generally located in remote areas.
The connection of DGs fundamentally alters distributionnetwork operation and creates a variety of well knownimpacts that range from bi-directional power flows toincreased fault levels although the capacity is predominantlylimited by the voltage rise in more rural networks [3]. Arange of options exist to mitigate adverse impacts, underexisting (deep-connection) UK commercial arrangementsbut the developer will largely bear the financial responsi-bility for their implementation. The economic implicationscan make potential distributed generation schemes lessattractive and has been an impediment to the developmentof renewable energy. Whereas the shallower connectioncharging regime to be introduced in April 2005 will lessenthis effect, the burden will increasingly fall on thedistribution network operators (DNOs) who will have tojustify their investment and tariff setting approaches [4]. Afurther issue is that current DNO policies of assessing DGconnections on a first-come first-served basis can limitholistic distributed generation development in that an earlyand perhaps quite minor connection can constrain devel-opment of other, potentially larger, opportunities in thesame area, effectively ‘sterilising’ parts of the network.
The need to mitigate DG-related effects occurs because ofthe mismatch between the location of renewable resourcesand the capability of the network in those areas to acceptnew generation. The problem is compounded since,currently, the connection process ignores the consequenteffects of decisions. One means of tackling both challengesis for the DNOs to encourage development at the most
The authors are with the School of Engineering and Electronics, Institute forEnergy Systems, University of Edinburgh, King’s Buildings, Mayfield Road,Edinburgh EH9 3JL, UK
r IEE, 2005
IEE Proceedings online no. 20041193
doi:10.1049/ip-gtd:20041193
Paper first received 4th September 2003 and in revised form 7th September 2004
suitable locations by issuing information to developersregarding the existence, or otherwise, of spare connectioncapacity or from the locational signals created by connec-tion pricing. Indeed it is a key driver in Ofgem’s RegisteredPower Zones initiative in order to ‘signal to potentialgenerators and other interested parties a DNO’s develop-ment intentions or network capabilities at a particularlocation’ [5]. The starting point for such initiatives is forDNOs to quantify the capacity of new DGs that may beconnected to distribution networks with and without theneed for reinforcement.
2 Capacity Evaluation
A recent study of the transmission system in Scotland haspublished guidance on the location and capacity of newrenewable generation that may be connected given currentand future investment in the network [6]. Given that asignificant fraction of new plant will connect to the sub-transmission or distribution network there is a clear need toperform similar studies at the distribution level. However,the greater influence of DGs on customer voltages (giventhe circuit and load characteristics and the relative lack ofautomatic voltage control), together with a significantlylarger number of connection points means that a study oneven a relatively small section of the distribution network islaborious. As such, evaluating distribution network head-room requires a means of effectively dealing with theproblems of multi-dimensionality without unduly increasingthe associated computational burden.
Among the literature devoted to optimisation problemsat the distribution level [7], several studies considertechniques for locating DG plant for optimal benefit. Rauand Wan [8] employ gradient and second-order methods todetermine the optimal location for the minimisation oflosses or line loading. Kim et al. [9] use a combination offuzzy non-linear goal programming and genetic algorithm(GA) techniques to locate DGs and minimise overall powerlosses. Nara et al. [10] apply tabu search techniques to thesame problem. Griffin et al. [11] demonstrate an iterativemethod that provides an approximation for the optimalplacement of DGs for loss minimisation. Within this,individual DG units were sited at selected buses and theimpact on losses examined. Locations were ranked on thebasis of loss reduction and DG units added incrementallyuntil losses were minimised. Finally, Celli and Pilo [12]present a GA approach that, for a defined planninghorizon, minimises the cost of network investment andlosses whilst meeting feeder thermal, voltage profile andfault-level constraints.
The wide fluctuation in demand (particularly at LV) and,an uncertain coincidence with distributed generation export,makes loss minimisation, in itself, a complex, real-timeissue. As such, the requirement here was to determine themaximum distributed generation capacity at one, multipleor all connection points whilst satisfying network physicaland operational constraints. Whereas many of theapproaches highlighted above might be adapted for thispurpose, the intention was to make most use of tools thatare already available and whose rigour is accepted byDNOs. Accordingly, optimal power flow (OPF) is em-ployed for the capacity evaluation.
3 Problem Formulation
OPF has been developed extensively through power systemsresearch to address problems ranging from economicdespatch to loss minimisation [13] and is a common feature
in many power flow packages. As such, the use of OPF tomaximise generation capacity appears to be a logicalapproach, however, it is not without difficulty. Thegenerator models traditionally employed in OPF are ofsynchronous generators operating in voltage control mode.Given that distributed generation is generally operated at aconstant, pre-set, power factor, there is a need to preservethe power factor during optimisation. Hence, alternativemodels are necessary and although this is feasible withbespoke OPF programs, it is generally not an option forproprietary packages.
To overcome this, the solution method presented heretakes advantage of the commonly used technique ofmodelling steady-state DGs as negative loads. OPF hasbeen used extensively for load shed optimisation in, forexample, finding the conditions for voltage collapse. In thiscase, however, the conditions for voltage and thermalviolation are of primary concern. By representing each DGas a negative load, the capacity evaluation problem can beexpressed as a load addition problem: essentially a negativeload shed where the cost of the negative load is minimised.This procedure has been termed ‘reverse load-ability’.
3.1 Mathematical FormulationThe capacity maximisation is adapted from an OPFformulation designed to minimise the cost of load shed[14]. The objective function (f) and associated formulae aregiven as follows:
min f ðcÞ ¼Xn
i¼1�Ci �MW 0
i ð1� ciÞ ð1Þ
subject to
cMIN;i � ci � cMAX;i ð2Þ
MVADGi ¼ ci �MVA0
i ð3Þ
VMIN;j � Vj � VMAX;j ð4Þ
Sk � SMAX;k � 0 ð5Þ
The capacity adjustment factor (c) controls the capacity ofeach DG (i) located at one of the n locations selected. It actsso as to multiply the initial machine active power capacity(MW0) by a value between the specified upper (cMAX) andlower (cMIN) limits, defined in (2). The negative capacitycost (�C) represents the benefit derived per unit megawattof distributed generation capacity connected and ensuresthat the optimisation delivers the most negative value forthe objective function. DGs possessing greater benefit arefavoured. Equation (3) ensures that the output of each DG(MVADG) remains at the same power factor as originallyspecified (MVA0). Clearly, different power factors, costsand capacity ranges can be specified for each potential DG.The allowable network voltage (at each bus j) and thermalconstraints (for each branch k) are given in (4) and (5),respectively.
3.2 ImplementationThe technique was implemented using the OPF componentof the PSS/E power flow package (which uses an interior-point method) together with a bespoke user interface tomanage the evaluations (termed the ‘simulation manager’).Further details of the implementation can be found in theAppendix. However, the formulation should be applicableto any OPF package with the capability to optimise busloading.
4 Available Capacity within an Extensive Distri-bution Network
The capacity evaluation technique has been applied toa section of the UK transmission and distributionnetwork with a voltage range of 11 to 400kV and around6000 and 600km of overhead line at 11 and 33kV,respectively. The network serves around 100 MVA ofload in a mainly rural setting and the region has exten-sive potential for on- and offshore wind, mini-hydro andother renewable energy developments. Furthermore, over300 MW of larger, centrally dispatched, generation islocated in the network. In many respects the distributedgeneration issues facing the network reflect those of the UKas a whole.
Figure 1 shows a sub-section of this network consisting ofthe 132kV sub-transmission system with five grid supplypoints (GSPs), labelled A to E, modelled down to the 11kVprimary substations. Buses 1 to 12 are at 132kV with thecircuit between buses 1 and 2 representing the primary routefor import and export from this portion of the system. TheGSP groups are generally radial (with only limited inter-group connections) and supply loads that range from ruralto urban-rural.
4.1 Analytical ConsiderationsWith several voltage levels present, the operation of auto-tap transformers to provide voltage control must beconsidered. The algorithms in power flow simulations alloweffective modelling of the auto-tap changers in meetingtarget secondary voltages. Unfortunately, such operation isnot normally defined within the OPF; rather, tap settingsare altered such that voltage profiles optimise the solution,i.e. by maximising export from the DG. This mode ofoperation, along with the practice of fixing taps (as used ininitial studies, e.g. [15]) was considered to be unrealistic,although this capability could enable examination ofpotential operational changes to accommodate moreDGs. The difficulty was resolved by constraining thesecondary bus voltages to their target values.
The evaluation was set up as follows: the capacityadjustment factor (c) was set to one, initially, within arange of zero to 100. With an initial active power capacityof 1 MW this allowed distributed generation capacities
of between zero and 100 MW. To ensure a balancedevaluation of capacity, the capacity benefit value was set tobe the same for all locations (although clearly this could beset differentially to reflect the costs and benefits ofalternative generating technologies). Testing found theoptimisation to be relatively insensitive to the actual valueso this was set, arbitrarily, to 100 units per megawatt.
The evaluation was subject to thermal and voltageconstraints. The applied thermal constraints were therelevant line and transformer ratings. A simplificationhas been made in setting the transformer reverse powerflow capability equal to that for the forward direction;some transformers have lower reverse capabilities and thesecan be incorporated through appropriate directionalflow limits. The maximum voltage limits are defined bystatute [16] although DNO planning policies (whichimplement relevant technical standards such as engineeringrecommendation (ER) P28 [17]) specify more rigorousconstraints in order to allow for the impact of upstreamvoltage variations, switching events, outages and tapchanger operation. To illustrate how voltage constraintsimpact on network headroom, two constraint policies wereapplied:
1. voltage range as allowed by statute, i.e. 76% at 11 and33kV, 710% at higher voltages:
2. voltage range of73% at 11 and 33kV (as defined by ERP28).
These applied to all locations other than the 11 and 33kVtransformer secondary buses that were constrained tonominal voltage.
The range of distributed generation sources is diverse andthis is reflected in the range of power factors that must beconsidered. Here, several scenarios are considered: genera-tion operating at power factors of 0.95 leading, 0.95 laggingor at unity. Load level also has a major bearing on thecapability of a network to accept generation: accordingly,conditions of 25 or 100% of maximum demand wereexamined.
The analysis of network headroom is illustrated usingindividual GSP groups and one group is analysed in detail.Following that, the analysis is extended to the network as awhole.
4.2 Capacity available in GSP groupsHere, each GSP group was considered as a stand-alone unitwithout inter-group connections. Whereas this will tend toreduce the maximum potential power export, the connec-tions are limited and are designed to facilitate supply in theevent of a loss of line. As such, the estimates of headroomwill be reasonably realistic. Firstly, GSP group A isconsidered in detail (with Table 1 providing the character-istics of the network in the vicinity of the GSP).
4.2.1 Group A: Estimates of the available capacity ateach 11kV substation in GSP group A were generated bylocating a negative load at each 11kV bus (buses 21, 23 and26) and executing the OPF. This was carried out for severalcombinations of power factor and load level and the resultsare given in Table 2 for statutory voltage constraints andTable 3 for the narrower 73% limits.
For the scenarios examined, there are significant varia-tions in the overall capability of the network to absorbpower injections ranging from 26.6 MW at a lagging powerfactor to 69.3 MW at a leading power factor. The effect of
load is significant and, as would be expected, the networkexhibits greater absorbence at higher load levels. Further-more, leading power factors tend to allow greaterabsorbence since the import of reactive power tends toease the voltage rise resulting from active power export and,accordingly, greater export may be achieved before voltagelimits are reached.
The pattern of capacity availability tends to indicate thetype of constraint restricting power injections at eachprimary bus. Where the capacity remains similar across therange of power factors this highlights a thermal limitation,e.g., bus 21 which, for all cases, is limited by the 8 MVAtransformer rating. Alternatively, buses showing signifi-cantly different capacities across power factors and,particularly at different allowable voltage ranges, arevoltage constrained with buses 23 and 26 as clear examples.
While this suggests the general rules, it masks significantdifferences between each of the scenarios in terms of theconstraints that apply. Figure 2 shows the portion of thenetwork in Fig. 1 that covers GSP A. It contains a subset ofthe scenarios from Tables 2 and 3, and details availablecapacity and the applicable constraints influencing thecapacity evaluation. Figures 2a and 2b are examples at 25%load and76% voltage range whereas Figs. 2c and 2d are at100% load and 73% voltage range.
Table 1: Characteristics of distribution network in vicinity ofGSP group A
Line (nominal voltage), kV Impedance, pu (Rating, MVA)
Buses 6 to 7, 6 to 8 (132) 0.022+j0.049 (132)
Bus 20 to 22 (33) 0.340+j0.454 (19)
Bus 20 to 24 (33) 0.258+j0.453 (27)
Bus 24 to 25 (33 0.872+j0.625 (11)
Transformers Ratings, MVA
Supply to bus 20 2� 60
Supply to bus 21 1� 8
Supply to bus 23 2� 24
Supply to bus 26 1� 5
Table 2: Capacity available in megawatt for DG connectionat 11kV primaries within GSP group A (voltage76%)
Bus 25% load/power factor 100% load/power factor
0.95 lag Unity 0.95 lead 0.95 lag Unity 0.95 lead
Bus 21 8.1 8.4 7.8 9.3 9.6 8.8
Bus 23 34.4 35.5 37.7 47.2 53.8 48.2
Bus 26 0.0 4.8 4.9 0.0 6.0 5.6
Total 42.5 48.7 50.4 56.5 69.3 62.5
Table 3: Capacity available in megawatts for DG connectionat 11kV primaries within GSP group A (voltage73%)
Bus 25% load/power factor 100% load/power factor
0.95 lag Unity 0.95 lead 0.95 lag Unity 0.95 lead
Bus 21 8.1 8.4 7.8 9.3 9.6 8.8
Bus 23 18.5 29.3 37.7 31.4 53.8 48.1
Bus 26 0.0 0.0 4.9 0.0 6.0 5.4
Total 26.6 37.7 50.4 40.7 69.3 62.3
7 8
33 kV20
25
2623
21
8.1 MW
34.4 MWa b
c d
0 MW
22 24 22 24
22 24 22 24
7 8
33 kV20
25
2623
21
7.8 MW
37.7 MW 4.9 MW
7 8
33 kV20
25
2623
21
8.1 MW
31.4 MW
G G
G
G G
G
G G
G
G G
G
0 MW
7 8
33 kV20
25
2623
21
7.8 MW
48.1 MW 5.4 MW
available DGcapacity3.1 MW
14 max. voltageon bus
G14 min. voltage
on bus
component atthermal limit
Fig. 2 Available network capacity and applicable constraints forGSP group A for four scenariosa The scenario of a 25% Load and a76%, voltage range with a 0.95leading power factorb The scenario of a 25% Load and a76%, voltage range with a 0.95lagging power factorc The scenario of a 100% Load and a73%, voltage range with a 0.95leading power factord The scenario of a 100%Load and a73%, voltage range with a 0.95lagging power factor
The predominance of voltage constraints for the leadingpower factor cases can be seen in Figs. 2a and 2c where thevoltage rise along the 33kV feeders results in bus voltages atthe far end (i.e. at buses 22 and 24) at the applied limits. Inboth cases the OPF favours capacity at bus 23 over bus 26.The dominance of thermal constraints for the lagging powerfactor cases is apparent in Figs. 2b and 2d where thetransformer supplying bus 26 and the line from bus 20 tobus 22 are at their limits. Furthermore, in Fig. 2d thesignificant reactive power demand from generation at buses23 and 26 are seen to depress the voltages along the bus 20to 24 feeder with the voltage at bus 24 as low as 0.97 pu. InFig. 2, the transformer supplying bus 21 is thermally limitedsince the direct connection to the GSP allows capacity to besited essentially independently of the rest of the network.
The analysis for group A (and all others) has notconsidered fault conditions in that parallel paths areassumed to be in-service. True worst-case scenarios wouldconsider capacity with one or more paths out-of-service.For example, in Fig. 2a with both transformers supplyingbus 23, export is constrained by the voltage on the 33kVfeeders. However, the loss of a transformer would limit netexport from bus 23 to the thermal rating of the remainingunit (24 MVA). The impact on the overall capacityavailable would depend on which transformer remained inservice; the loss of that connecting buses 24 and 23 wouldpotentially allow additional export from bus 26. The non-consideration of fault conditions should be borne in mindwhen interpreting the capacities suggested in the remainderof the analysis.
4.2.2 Groups B to E: Given the network size, there isinsufficient room to allow detailed analyses of GSP groupsB to E. However, an examination of the overall capacitiesavailable at the 11kV primaries within each GSP providesan understanding of the constraints on distributed genera-tion development in the relevant portion of the network.These capacities are summarised in Tables 4 and 5 for thesame voltage limits as above.
As before, it is possible to infer the constraints thatdominate within each GSP group. Hence, from Tables 4and 5 we can see that capacities within group A are heavilyinfluenced by voltage constraints, group E is also partlyvoltage constrained albeit to a lesser degree. The relativelysmall differences between available capacities in groups B toD highlight thermal limitations, with e.g. group B limited bythe rating of the 132/33kV GSP transformer (connectingbus 6 and bus 30). In group D, the lower capacity availableat a leading power factor is due to the reactive powerimport along the relatively long parallel feeders ‘crowdingout’ active power export.
While it can be misleading to infer network characteristicsfrom one line diagrams (as they do not convey distance orimpedance information), the topology of the various groupscan be related to the pattern of available capacity and theconstraint patterns. Export along sets of meshed double-circuit feeders tend not to be voltage constrained, rather therating on the 33/11kV primary transformers are the limitingfactor (e.g. group D). Conversely, paths with several longsingle-circuit spurs tend to suffer voltage limitation (e.g.groups A and E).
It should be noted that for one condition in each ofTables 4 and 5 the capacity available is marked ‘n/c’. Thisrefers to the non-convergence of the OPF solution as aresult of the inherent voltage sensitivity of the distributionnetwork. It occurs in group D, where the additional reactiveimport from a DG operating at a 0.95 leading power factoron top of that drawn by load at its maximum level, resultsin voltage collapse and, consequently, non-convergence.
4.3 Analysis of the entire systemIn evaluating the capacity available across the whole of thenetwork, an identical approach to that for the GSP groupswas taken. Here, potential generation was sited at each11kV primary throughout the system and the OPFexecuted for conditions of maximum load with severalpower factors. The overall capacity accommodated and thebreakdown between groups is given in Table 6 and showngraphically in Fig. 3.
It is clear that the capacities indicated at each GSP aresignificantly below those accommodated at each GSP whenconsidered individually (see Table 4). The reason for this isevident given that the overall capacity is essentially the sameacross each of the power factor options: characteristic of athermally constrained system. This is the case as it is therating of the circuit joining buses 1 and 2 that is the limitingfactor in connecting further capacity. To accommodatefurther DGs would require upgrading of that circuit and/orretiral of existing conventional generation.
Table 4: Maximum DG connections in megawatts withinGSP groups under statutory voltage constraints
GSPgroup
25% load/power factor 100% load/power factor
0.95 lag Unity 0.95 lead 0.95 lag Unity 0.95 lead
A 42.5 48.7 50.4 56.5 69.3 62.5
B 23.0 22.6 20.4 31.0 30.2 24.9
C 39.7 39.0 39.6 57.7 57.3 39.7
D 121.4 126.0 115.8 157.1 158.5 N/C
E 76.5 80.7 79.8 107.1 115.3 102.2
Table 5: Maximum DG connections in megawatts withinGSP groups under 3% voltage constraints
GSPgroup
25% load/power factor 100% load/power factor
0.95 lag Unity 0.95 lead 0.95 lag Unity 0.95 lead
A 26.6 37.7 50.4 40.7 69.3 62.3
B 22.6 22.8 20.4 30.7 30.1 24.9
C 38.9 38.8 39.9 57.0 58.1 56.3
D 121.4 126.0 115.8 157.1 158.5 N/C
E 62.0 74.5 65.6 92.5 100.7 101.7
Table 6: Maximum connections in megawatts at 11kV forentire network at 100% load and within statutory voltagelimits
The evaluations of capacity presented here represent singleanalyses at one point in time. Development is resource-ledand so is unlikely to occur at the locations and in thecapacities necessary to fit neatly with evaluations of capacitysuch as these. As such, DNOs need to re-evaluate capacityafter each new connection and project the impact on futurepotential connections with and without reinforcement. Theability to evaluate connections in this manner allowsplanners to consider the downstream impact of connectiondecisions in terms of the network capacity that is taken upat each stage in the process. This process is illustrated usingGSP group A as an example with the DG at a 0.95 laggingpower factor, load at 25% and with statutory voltageconstraints.
Section 4.2.1 considered capacity available across allgroup A primaries as a whole. However, in considering asequence of connections it is enlightening to consider thecapacity available at each primary, individually. Asmentioned earlier, bus 21 is essentially independent of theother two primaries and the capacity available is limited bythe transformer rating (to 8.1 MW). Since the evaluationfavours generation at bus 23 (34.4 MW, see Table 2) to theexclusion of bus 26, this also represents the maximumavailable at bus 23 alone. The capacity at bus 26 alone wasfound by executing the OPF with a negative load at this busonly and resulted in a capacity of 5.1 MW. Theinterdependence of buses 23 and 26 arises from their jointcontribution to the voltage rise along the feeder from theGSP. As such, capacity at one must be traded-off againstcapacity at the other.
It is possible to examine this trade-off by deliberatelysiting capacity at bus 26 and evaluating the capacity at theother primaries. This is an identical situation to that where adeveloper has received a connection agreement and, as such,possesses prior access rights; any subsequent connectionsmust be considered with the distributed generation inoperation. Figure 4 and Table 7 show the resultingavailability of capacity in the network as the priorconnected capacity at bus 26 rises from zero to 5 MW. Itis clear that as the prior capacity rises, the available capacityat bus 23 falls (bus 21 remains static). More importantly,the reduction in capacity at bus 23 is greater than theincrease at bus 26, hence, the overall capacity that may beconnected in the network falls. Table 7 indicates that forevery megawatt of prior capacity added there is in theregion of 3.3 MW of capacity lost at bus 23. Increasing
prior capacity to the maximum 5.1 MW would completelyremove capacity at bus 23. Clearly, the optimal combina-tion of capacity is to site nothing at bus 26 and themaximum amount at bus 23.
This relatively simple example illustrates the conse-quences of inappropriately sited connections in terms oftheir ability to constrain capacity and eventually sterilise thenetwork. This effect, spread across the entire distributionnetwork represents a significant threat to the target ofmaximising distributed generation penetration to achieverenewable energy targets. Clearly, the ability of DNOs toinfluence future developments will depend very much ontheir own internal policies and that of the regulator towardsdistribution network access.
6 Discussion
The OPF-based techniques presented are a valuableaddition to the set of planning tools potentially availableto DNOs. They provide a rapid, adaptable and objectivemeans of examining the connection of DGs and willprovide information regarding the most suitable sites toconnect DGs. The technique demonstrated has beenevaluated on an extensive distribution and sub-transmissionsystem and is applicable at all voltage levels. Using anextensive network (and its component parts) the capacityevaluations illustrate many of the issues surrounding theconnection of DGs and the constraints on development.
OPF has been in use in power systems research, planningand operation for many years. As such, this techniquerepresents an acceptable and logical extension of the use of
120
100
80
60
avila
ble
capa
city
, MW
40
20
00.95 lag 0.95 lead
power factor
group E group D group C group B group A
unity
Fig. 3 Available capacity at 11 kV within each GSP group for thenetwork as a whole
45
40
35
30
avai
labl
e ca
paci
ty, M
W
25
20
15
10
5
00 1 2
prior capacity at bus 26
bus 21 bus 23 bus 26
3 4 5
Fig. 4 Impact of prior connected capacity at bus 26 on GSP groupA available capacity
Table 7: Available capacity in megatwatts at 11kV withinGSP group A with prior connected capacity at Bus 26
OPF. One of the potential benefits of performing the OPFanalysis within a proprietary software package is that theanalysis can be readily extended to incorporate multipleobjectives such as loss minimisation or the simultaneousoptimisation of existing generation.
Although not explicitly demonstrated here, use of thesimulation manager limits time-consuming data entry andallows users to concentrate on more productive taskssuch as determining the combinations of locations at whichgeneration may be sited and the relevant constraint policyfor connections. Furthermore, the sequential evaluation ofcapacity following successive additions of plant is madestraightforward.
In addition to identifying the location and magnitude ofavailable network capacity, the OPF allows the limitingfactors to be highlighted, which may be equipment thermalratings or the specified voltage constraints. It is anticipatedthat the identification of the limiting factors together withthe Lagrangian coefficients within the OPF could be used toprovide an efficient and effective means of determiningnetwork upgrades and reinforcement that allow furtherDGs to be accommodated.
Currently, this technique cannot take account of fault-level constraints when evaluating available capacity,although a fault-level study can clearly be run followingthe OPF to ascertain if constraints have been breached.These may predominate in urban meshed networks but aregenerally not a major limitation for rural feeder systems.The GA approach presented in [12] may offer one means ofincorporating fault-level constraints but the authors haveextended an OPF to impose fault-level restrictions on thecapacity evaluation [18].
One potential criticism of the approach taken here isthat it uses a single deterministic optimisation. Thiswas developed as current UK connection practice is basedon the worst-case scenario. However, as execution of theevaluation is very rapid (less than 1 s) it is feasible to use itfor time-series analyses with typical patterns of demand orwind spectra.
7 Conclusions
Government-led targets and incentives will increase thecapacity of distributed generation connecting to distribu-tion networks. Distribution-level connections create anumber of technical problems that may be mitigatedfor individual connections, albeit at a cost to the deve-loper or network operator. With anticipated connectionvolumes, there is a potential risk of conflict betweenconnections, in that inappropriately sized or located plantcould constrain further development of the network and,consequently, threaten the achievement of renewable energytargets.
One means of addressing this risk is to encouragedevelopment at sites that are suitable and at the same timediscouraging inappropriate ones. In doing so networkoperators must be able to evaluate the capacity orheadroom available on the system. In meeting this challengea technique has been developed that models fixed-powerfactor distributed generation as negative loads and usesoptimal power flow to perform negative load shedding. This‘reverse load-ability’ approach effectively maximises capa-city and identifies available headroom.
The technique was evaluated using an extensive distribu-tion and sub-transmission network and is found to rapidlyidentify available headroom within the imposed thermaland voltage constraints. Additionally, the technique wasused to examine the consequences of a sequence of
connections in terms of the impact on available headroomand in sterilising the network. Overall, the approachappears to be a valuable addition to available networkplanning tools.
8 Acknowledgments
The authors acknowledge, with gratitude, the supportprovided by EPSRC (grant GR/N04744) and ScottishPower plc. They are grateful also for the assistance of thestaff at Power Technologies Ltd. The reviewers’ insightfuland positive comments were very welcome.
9 References
1 Department of Trade and Industry: ‘The Renewables ObligationOrder 2002’ (Stationary Office, London, 2002)
3 Masters, C.L.: ‘Voltage rise: the big issue when connecting embeddedgeneration to long 11kV overhead lines’, Power Eng. J., 2002, 16, (1),pp. 5–12
4 Office of Gas and Electricity Markets: ‘Structure of electricitydistribution charges: initial decision document’ (Ofgem, London,November 2003)
5 Office of Gas and Electricity Markets: ‘Innovation and RegisteredPower Zones: a discussion paper’ (Ofgem, London, July 2003)
6 Scottish Executive: ‘Impact of Renewable Generation on the ElectricalTransmission Network in Scotland’ (Scottish Executive, Edinburgh,2001)
7 Nara, K., and Song, Y.H.: ‘Modern heuristics application todistribution system optimization’. Proc. IEEE Power Eng. SocietyWinter Meeting, 2002, pp. 826–832
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10 Nara, K., Hayashi, Y., Ikeda, K., and ASHIZAWA, T.: ‘Applicationof tabu search to optimal placement of distributed generators’. Proc.IEEE Power Engineering Society Winter Meeting, 2001, pp. 918–923
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10 Appendix
10.1 Problem implementationThe reasoning behind the choice of the PSS/E package wasthreefold. Firstly, it is commonly used in industry and willtherefore be readily available. Secondly, it possesses an OPFcomponent with bus loading capability. Thirdly, it has thecapability to automate procedures via its internal program-ming language (IPLAN).
Automation significantly benefited the operation andcontrol of the OPF and earlier analytical approaches in
terms of accelerating the execution of the relevantprocedures. However, the repetitive process of datapreparation, execution, results extraction and analysis wasstill time-consuming and error prone, given the largenumber of parameters and data required.
An elegant solution to these issues came from thedevelopment of a bespoke graphical user interface to thePSS/E package. There is no direct interaction betweenthe two pieces of software; rather the control software isused to execute specific predefined routines by initiatingPSS/E and exchanging data through suitably formatted textfiles. The relationship and data flows between the twopackages are shown graphically in Fig. 5. In addition torelieving the problems identified earlier, the simulationmanager has also facilitated the integration of non-network-related data and allowed complex analyses to be conductedwith relative ease. While the simulation manager wasdeveloped for DG network analysis, the techniquesestablished are readily portable to other tasks addressedwith PSS/E.
simulation manager
serialdata
networkdata
controldata results
savedcases
IPLAN
PSS/E
Fig. 5 Control/data flows between simulation manager and powerflow software
