Wind Power Grid Operation Dr Geoffrey Pritchard University

Wind Power & Grid Operation Dr. Geoffrey Pritchard University of Auckland

Wind forecasting • Generation dispatched 2 hours in advance of real time. Forecasts used for loads, wind. • Re-dispatch / frequency-keeping required when forecasts turn out to be wrong. • Need to understand probability distribution of forecast error.

Centralised Data Set • Electricity Commission data. • Includes half-hourly metered output by all power stations, including wind farms. • Useful data for – – Tararua I (4 years), I+II (3. 8 years), III (8 months) Te Apiti (3. 4 years) Hau Nui (2 years) White Hill (6 months)

Unforecasted component of wind • The relevant quantity for grid operation is Unforecasted output = (actual output) – (forecast output) • Forecast is usually simple persistence forecast no change). (i. e.

Simple persistence forecast @ -2 hr TP-5 0 TP-4 TP-3 • Generator offers close • Wind forecast is actual output in TP-5 TP-2 TP-1 +30 min TP • Actual wind observed Unforecasted wind in TP = (output in TP) – (output in TP-5)

Scenario selection problem • NZ wind farms: 6 -30 distinct sites • Need a tractable collection of model scenarios for unforecasted wind at all sites • Historical data: too many/too few scenarios

Example: 2 sites Reduce the following to 5 model scenarios:

Example: 2 sites Reduce the following to 5 model scenarios:

2 sites, transmission unconstrained Only the total unforecasted output matters – so we really have only 3 scenarios

2 sites, transmission unconstrained An improvement – 5 different scenarios

Optimal dispatch problem (SPD) Generators offer to sell tranches qi, asking prices pi We find dispatches xi to minimize S pi x i (cost of power, at offered prices) so that – (forecast) demand is met – transmission network is operated within capacity – 0 < x i < qi

Deterministic dispatch problem minimizex c(x) (dispatch decision) (Cost of dispatch, valuing power at offered prices. )

Stochastic dispatch problem minimizex E[ c(x, W) ] (dispatch decision) (random wind/load outcome) (Expected cost of dispatch and re-dispatch. )

Example Thermal B: 100 @ $45 Wind B: 60 @ $0 Wind A: 60 @ $0 Hydro: 50 @ $42 60 @ $80 Thermal A: 100 @ $40 capacity 150 Load 264 Wind farm offers are forecasts only.

Dispatch solution Thermal B: 100 @ $45 45 Wind A: 60 @ $0 60 60 Thermal A: 100 @ $40 69 30 145/150 Load 264 (different from the standard optimal dispatch) Wind B: 60 @ $0 Hydro: 50 @ $42 60 @ $80

Wasserstein distance Distance between a true probability distribution m and a model-scenario representation n: d. W(m, n) = Em[ distance to nearest model scenario ]

Wasserstein distance • Wasserstein approximations are good for stochastic optimization in general, i. e. devoid of the context of a particular problem. • Can adapt it for a more specific class of problems by re-defining the distance between scenarios.

A way forward? • First solve the dispatch problem using only forecast wind (SPD). • Then generate relevant model scenarios for unforecasted wind at all sites. • Now re-solve allowing for re-dispatch costs created by the model scenarios (robust solution).

Wind Power & Grid Operation Dr. Geoffrey Pritchard University of Auckland

NZ has a large wind resource • 500 MW now installed or under construction – many more sites under investigation or seeking consents. • 3000+ MW potential – but this ignores system integration issues.