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Current GB SQSS Approach

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Presentation on theme: "Current GB SQSS Approach"— Presentation transcript:

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

2 This Presentation Overview of current SQSS methodology
Interpretation of Planned Transfer and Required Transfer Variations on SQSS approach Comparison and Conclusions Overview of the approach followed presently. Something about PT and RT and what they mean. Know that current method is limited – considered how it could be adapted or modified to work better for wind. Results from variations and comparison.

3 Approach 1 - SQSS Methodology
Current method with wind AT = 0.72 Section 1.1, Appendix 3 Different exporting and importing area wind AT (0.72/0.05) Section 1.3.1 Variable wind A-factors Section 1.3.2, Appendix 4 These variations are: Called approaches 1(a), (b) and (c). Link to report sections.

4 Current SQSS Methodology
1(a) – 1 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 Approach 1(a) MITS requirements at ACS peak Boundary splitting system into two parts – transmission capacity across this boundary. PT refers to a system (at winter peak) with PM <= 20% and generation scaled to meet demand. 2-Stage process: PT and IA

5 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 PT refers to a system (at winter peak) with PM <= 20% and generation scaled to meet demand. Scaling sets up PT condition. PT now exists on any circuit or boundary in the network.

6 Ranking Order Example For ACS demand of 60GW Less likely to run
Unit or Module Registered Capacity (MW) Contribution to Plant Margin (MW) Cumulative Capacity (MW) Unit 1 500 Windfarm A 600 0.4  600 = 240 740 Windfarm B 200 0.4  200 = 80 820 . . . Unit J 71900 Unit K 72100 Unit L 100 72200 Less likely to run

7 Wind Equivalent in Ranking Order
Average P available from equivalent thermal unit Average P available from wind generation Wind generation registered capacity Average availability of a thermal unit (At  0.9) Convert each wind farm, or group of wind farms, to an equivalent thermal unit. Equivalent thermal unit has same average power availability as wind generation. Assume wind will always run at maximum available power, therefore, average available power = load factor (in winter). Divide by thermal available power to convert to equivalent thermal unit (full registered capacity is used in ranking order). Mention that 36% is based on data measured in Southern Scotland. Load factors were: Winter: 36% Summer: 23% Year: 30% Registered capacity of equivalent thermal unit Wind generation winter load factor (LWind  0.36) Re = 0.4 RWind

8 (Applies to entire network)
Straight Scaling 1(a) – 5 Power output of generator i of type T Registered capacity PTi = S  AT  RTi Match generation and demand (Applies to entire network) Availability at ACS peak Apply when PM has been reduced to <= 20% by ranking order technique. All contributory generation is scaled. Imports/exports like France or NIE are ignored here. However, they are not scaled, but are treated as demand (Pos = export, Neg = import). If all AT are the same, its value does not matter; S x AT will be the same. S = 1/1.2 = 0.833 With a plant margin of 20% and AT = 1.0, S = 0.833

9 Availability Factors SQSS does not prescribe AT values
Thermal and hydro units: AT = 1.0 Wind generation: AT = 0.72 Sometimes hydro AT = 0.96 (P  0.80 in PT) If plant margin is exactly 20%…

10 Planned Transfer Example
RTi = MW D1 = 6000 MW G1 = 8333 MW AREA 1 PT = 2333 MW AREA 2  RTi = MW D2 = MW G2 = MW PT would now exist on any boundary. PM = 20%, so S = and total generation is 72GW. PM (North) = 67%, PM (South) = 14.8% ** Shown how PT is set up, now moving on to application of IA. System in Planned Transfer condition Total ACS peak demand = 60GW

11 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 Applying IA to the system. Steps to set up system with IA or IA/2. Under these conditions, for secured event (N-1, N-2, busbar, mesh corner), there may not be loss of demand unacceptable overloading voltages outside limits system instability

12 1(a) – 9 Circle Diagram Origins a bit hazy, but reported to be based on observed transfers in the 1940’s. Reviewed in early 1990’s and found to still be appropriate for system. See appendix D in SQSS. Total ACS peak demand = D1+D2 D1+G1 = Dem+Gen in small area (Area 1 in example).

13 IA Application Example
Circle diagram x-axis: RTi = MW D1 = 6000 MW G1 = 8333 MW AREA 1 PT = 2333 MW AREA 2  RTi = MW D2 = MW G2 = MW Find IA from circle diagram N-1: 3593MW N-2: 2963MW y-axis: 2.1% IA = 1260 MW System in Planned Transfer condition

14 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

15 Actual Boundary Transfer
PT PT + IA PT + ½IA Frequency PT is the median transfer. Depending on actual demand and generation outcomes, the boundary transfer could be higher or lower (even negative). PT+IA/2 gives around P95 (but, N-2/D less likely than N-1) PT+IA gives around P99 (but, more likely than N-2/D) Wind will change the shape/parameters of the distribution – we want to keep N-1 and N-2 probabilities/percentiles constant. With wind, PT and the shape of the distribution will be different, but we want to approximately maintain PT+IA/2 at P95 and PT+IA at P99. Boundary Transfer Expected boundary transfer at ACS peak

16 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

17 Different Export and Import Wind A-factors
1(b) – 1 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 Approach 1(b) We are trying to capture the high 5-10% of transfers where available power in Area 1 is much higher than the available power in Area 2. I.e. when the imbalance in available power is highest. This should include imbalance due to wind conditions. An important side effect of this is that the PT now becomes boundary dependent, i.e. a different PT needs to be set up for each boundary to study.

18 Importing Wind A-factor
1(b) – 2 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 AT = 0.05 (around 0.05  = 0.04 in PT) Approach 1(b) Different (but constant) A-factors Exporting area AT = 0.72 for wind (60% in PT) Importing area AT = 0.05 for wind (4% in PT)

19 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. Consider a particular boundary. Assumption is that N-1  P99 and N-2  P95. Simulation was carried out for all boundaries. Simplified simulation was used to determine A-factor functions; these were then applied to scenario.

20 Exporting Area Wind A-Factor

21 Importing Area Wind A-Factor

22 Results for 2007/8 RT (MW) Results for Approaches 1(a), 1(b) and 1(c)

23 Results for 2020/1 RT (MW)

24 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 For high-wind boundaries, economics are very likely to justify more transmission.

25 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 Approaches 1(b) and (c)

26 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 (AT = 0.72 or 0.05)

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