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– Workshop 3 – Birmingham, 10 January 2008 Current GB SQSS Approach Cornel Brozio Scottish Power EnergyNetworks

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This Presentation Overview of current SQSS methodology Interpretation of Planned Transfer and Required Transfer Variations on SQSS approach Comparison and Conclusions

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Approach 1 - SQSS Methodology a)Current method with wind A T = 0.72 Section 1.1, Appendix 3 b)Different exporting and importing area wind A T (0.72/0.05) Section 1.3.1 c)Variable wind A-factors Section 1.3.2, Appendix 4

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Current SQSS Methodology Transmission boundary capability at ACS peak Planned Transfer (Appendix C of SQSS) Interconnection Allowance (Appendix D of SQSS) Required Capacity = PT+IA 1(a) – 1

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Setting up Planned Transfer Ranking Order technique Set Plant Margin 20% Assumption is that market will deliver around 20%, but many closures are unknown Plant least likely to run is treated as non-contributory Straight Scaling technique Scale generation to meet demand Scaling proportional to availability at time of ACS peak 1(a) – 2

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Ranking Order Example Unit or Module Registered Capacity (MW) Contribution to Plant Margin (MW) Cumulative Capacity (MW) Unit 1500 Windfarm A600 0.4 600 = 240 740 Windfarm B200 0.4 200 = 80 820... Unit J200 71900 Unit K200 72100 Unit L100 72200 For ACS demand of 60GW Less likely to run 1(a) – 3

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Wind Equivalent in Ranking Order Average availability of a thermal unit ( A t 0.9) Registered capacity of equivalent thermal unit Wind generation winter load factor ( L Wind 0.36) Wind generation registered capacity Average P available from equivalent thermal unit Average P available from wind generation R e = 0.4 R Wind 1(a) – 4

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Straight Scaling Power output of generator i of type T P Ti = S A T R Ti Availability at ACS peak Registered capacity Match generation and demand (Applies to entire network) With a plant margin of 20% and A T = 1.0, S = 0.833 1(a) – 5

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Availability Factors SQSS does not prescribe A T values Thermal and hydro units: A T = 1.0 Wind generation: A T = 0.72 1(a) – 6

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Planned Transfer Example AREA 1 AREA 2 R Ti = 10000 MW D 1 = 6000 MW G 1 = 8333 MW R Ti = 62000 MW D 2 = 54000 MW G 2 = 51667 MW PT = 2333 MW System in Planned Transfer condition Total ACS peak demand = 60GW 1(a) – 7

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Interconnection Allowance Planned Transfer condition set up Select boundary, i.e. split system into two parts Find IA from the Circle Diagram Boundary capability: PT + IA for N-1 PT + ½IA for N-2 or N-D 1(a) – 8

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Circle Diagram 1(a) – 9

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IA Application Example AREA 1 AREA 2 R Ti = 10000 MW D 1 = 6000 MW G 1 = 8333 MW R Ti = 62000 MW D 2 = 54000 MW G 2 = 51667 MW PT = 2333 MW System in Planned Transfer condition Circle diagram x-axis: y-axis: 2.1% IA = 1260 MW 1(a) – 10

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What does the IA provide? Capacity for a generation shortage in one area to be met by importing from another area (most of the time) N-2 or N-D requirement (PT+½IA) can be met for 95% of actual generation and demand outcomes at ACS peak, assuming Enough generation in the exporting area No local constraints

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PTPT + IAPT + ½IA Expected boundary transfer at ACS peak Actual Boundary Transfer Frequency Boundary Transfer

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Variations Considered for Wind Keep PT+IA and PT+½IA at same percentile of possible boundary transfers Probabilities of exceeding N-1 or N-2 capabilities remain broadly constant Variations considered: Approach 1(b): Different wind A-factors for importing and exporting areas Approach 1(c): Variable wind A-factors based on wind volumes in each area

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Different Export and Import Wind A-factors PT+½IA captures all but the highest 5% of boundary transfers When imbalance in available power is highest Should include imbalance due to wind conditions At 60% in PT, support from wind generation in importing area is over-estimated 1(b) – 1

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Importing Wind A-factor In exporting area 60% is approximately P90 of wind output Mirror exporting area by using P10 of wind generator power output: About 4% of rated capacity A T = 0.05 (around 0.05 0.833 = 0.04 in PT) Approach 1(b) Different (but constant) A-factors Exporting area A T = 0.72 for wind ( 60% in PT) Importing area A T = 0.05 for wind ( 4% in PT) 1(b) – 2

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Approach 1(c): Variable Wind A-factors Aims to find A-factors as functions of relative wind generation volumes for any boundary Monte-Carlo simulation to find distribution of transfers and find P99 and P95 Using SQSS approach for same boundary, adjust wind A-factors until PT+IA (N-1) matches P99 and PT+½IA (N-2) matches P95 with minimum error.

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Exporting Area Wind A-Factor Wind A-Factor 1(c) – 2

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Importing Area Wind A-Factor Wind A-Factor 1(c) – 3

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Results for 2007/8 RT (MW)

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Results for 2020/1 RT (MW)

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Summary Approach 1(a) – Single A-factor (0.72) Works well, but over-estimates wind contribution in importing area Approach 1(b) - Different A-factors (0.72/0.05) Extends existing approach System security remains broadly constant I.e. probability of exceeding N-1 or N-2 capability remains approximately constant Approach 1(c) - Variable A-factors Difficult to find robust A-factor functions (scatter on graphs) Additional complexity Except high-wind export boundaries, very similar RT to constant 0.72/0.05

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Drawback - Different PT for each Boundary Both variations of SQSS approach mean that PT becomes boundary dependent Different A-factors in each area Single PT condition no longer exists Importing and exporting areas not always clear By exchanging A-factors, direction of PT can be reversed

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Recommendation As at present, approach would remain supported by cost-benefit analysis If existing SQSS approach is to be retained, adopt Approach 1(b) Different (but constant) A-factors in exporting and importing areas ( A T = 0.72 or 0.05)

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