Integrating Renewables into the Electricity System An Historical Overview Professor Michael Laughton Centre for Energy Policy & Technology, Imperial College Open University Jan 24th 2006

Renewable Energy Sources Marine Wave (onshore, offshore) Tidal (barrage, stream) Hydro Large-scale, Small-scale WindOnshore, Offshore SolarPassive-, Active-heating, Photovoltaic GeothermalHot-dry rocks, aquifers BiofuelsWaste, Crops, Landfill gas (combustion, conversion)

Characteristics of variability Fluctuations and possible power supply shortages due to: Uncertainties in prediction of occurrence Power conversion plant limits - too little resource availability - too much resource availability Magnitude of fluctuations - small - large Speed of fluctuations - slow (usually predictable) - fast (less predictable) Examples are as follows: -

Typical annual variation in wave power levels

Wind Turbine Output Characteristic Wind Speed m/s % Rated Output

Slow strong fluctuations Reference: EON_Netz_Windreport_e_eng.pdf

Fairly rapid decrease Winter power infeed E.ON control area to Reference: EON_Netz_Windreport_e_eng.pdf

Euros / MWh Fast fluctuations Danish electricity spot prices first week of January 2005

Met Office data for wind speeds archive/uk/ Note: 9 knots = 4.63 m/s (Wind Turbine cut-in speed approx)

Note, however,…….. Many Met Office stations are geographically irrelevant for judging wind power potential in Britain. Wind speeds at Met Office station monitoring heights need to be increased to account for variation of speed with height. A very simple rule might be V z = V h (z / h) a where V z and V h are wind speeds at heights z and h, h>z and a = 0.16 Ref: Halliday and Lipman, 1982

“Economic and Operational Assessment of Intermittent Generation Sources on Power Systems” Colloquium EE Dept, Imperial College, 5 March 1987 Contributions from: E.D.Farmer (Imperial College from CEGB) D.J.Milborrow (CEGB) J.P Palutikof, C.P.Watkins (UEA) S.C.Ryrie (Bristol Poly) D.T.Swifthook (CEGB) P.R.Hanson (CEGB) M.J.Grubb (Imperial College) A.Thorpe (CEGB) D.G.Infield, J.A.Halliday (Rutherford-Appleton Laboratory)

Cubed values of annual mean wind speeds at Southport Marshside proportional to wind turbine power output Ref: J.P.Palutikof, C.P.Watkins, “Some Aspects of Wind speed Variability….”, Op. Cit

Probabilistic electricity generation analysis is needed to determine capacity credit ONLY direct time series analysis of historical data of wind combined with probabilistic analysis of the availability of thermal units can hope to capture the real capacity credit of wind. The risk of system failure within a few GW of peak demand is not much less than at peak demand, BECAUSE the thermal plant output may have a standard deviation of between1 and 2 GW. Ref: M.J.Grubb, “Capital Effects at Intermediate and Higher Penetrations”, Op Cit

Probabilistic electricity generation analysis is needed to determine capacity credit Small capacity shortages have a much higher probability than large shortages and have little effect on security of supply, BUT as the capacity of wind in the system increases, the capacity credit is increasingly dominated by the smaller likelihood of little or no output. Ref: M.J.Grubb, “Capital Effects at Intermediate and Higher Penetrations”, Op Cit

Baseload capacity displacement with increasing wind penetration Ref: M.J.Grubb, “Capital Effects at Intermediate and Higher Penetrations”, Op Cit Variations with peak availability, diversity, system limiting costs Conclusion:- As a ‘Rule of Thumb’ the capacity credit for wind in Britain is the square root of the GW of wind installed

0 7,200 6,400 5,600 4,800 4,000 3,200 2,400 1, P e r c e n t a g e Total wind power generation distribution to achieve half Government 2010 target Source: National Grid PIU Supplementary Submission 28 Sept 02 TM / ML / Percentage of time over a 5 Year Period Average hourly generated power MW

Comment The above slide is key to understanding wind capacity credit. It is based on a study by National Grid of ten years of hourly Met Office data for sites relevant to the mainland Britain power transmission system. The graph shows the probability of actual wind power generated per annum from 7600 MW of installed capacity assuming no transmission constraints.

Further National Grid studies Purpose - to establish reliability of supply with increasing wind penetration. The following charts show the probability density distributions of the availability of the extra capacity needed to maintain security of supply levels. The first chart relates to the conventional thermal plant planning margin of 19% calculated from plant availability statistics. The third chart shows the influence of the graph shown on the previous slide relating probabilities of wind power output to installed wind capacity in a combined probabilistic analysis of existing thermal plant and wind capacity. This next slide and also the slide following draw attention to the implications for capacity credits of wind and the need to maintain conventional plant capacity. The results are similar to those obtained by M.Grubb in the 1980’s.

Total generation capacity for secure supply 500 MW wind 59,000 MW conventional Spare capacity = 9.5GW 7,500 MW wind 57,000 MW conventional Spare capacity = 14.5GW 25,000 MW wind 55,000 MW conventional Spare capacity = 30GW Zero indicates generation balances load. Area to left of zero is the probability of not meeting 50,000 MW peak demand 10 winters per century Source: NGC

National Grid generation capacity calculation to maintain security of supply standards – a different presentation 25,000 MW Wind 25,000 MW Thermal Thermal planning margin reduced by 4,500 MW 25,000 MW Thermal Plant Capacities Note: The thermal capacity not displaced has also been called standby or shadow capacity The extra system support costs relate to the net 20,500 MW of thermal capacity not displaced 20,500 MW extra

Further results from the ILEX study for the DTI on system costs of additional renewables (the SCAR Report In this study equivalent thermal capacity was removed against the energy contributions from increasing wind capacity assuming no transmission limitations exist. A combined probabilistic simulation of operation then established the levels of extra standby plant needed. This is a capacity remix that was not present in the studies of Grubb and the National Grid shown above. Overall the results confirm that large amounts of wind power need large amounts of conventional plant to be retained. It is not clear what to call this retained capacity. ‘Standby capacity’ has been used by the Royal Academy of Engineering and its equivalent replacement costed accordingly. The German utility E.ON Netz refer to it as ‘shadow capacity’. Obviously the ‘standby costs’ would be very different from those quoted in the SCAR Report. The astonishing conclusion from all of these studies (Grubb, National Grid, ILEX) is that regardless of the wind capacity in the system, the conventional capacity needed always exceeds the peak demand.

Demand growth scenarios with various penetration levels of wind energy by 2020 Peak demand 75,700 MW; Other renewables 1,600 MW TotalInstalled ConventionalConventional Spare Spare wind wind capacitycapacity capacity capacity energycapacity requiredmargin margin margin % MW MW % MW % , , ,900 86, , ,000 84, , ,000 82, , Ref: “Quantifying the System Costs of Additional Renewables in 2020”, ILEX Energy Consulting Report to the DTI, October 2002.

Conclusions - Integrating Renewables into the Electricity System Need to know more about the interaction of rates of change, magnitudes and lengths of intermittency with conventional plant needs, system constraints (loadflow / transmission constraints, voltage and frequency control), effects of groupings of wind generation with regard to capacity credit, how to define such groups (size, spread, location, geographical orientation,….), etc, etc…..