APPLICATION OF COST FUNCTIONS FOR LARGE SCALE INTEGRATION OF WIND POWER USING A MULTI-SCHEME ENSEMBLE PREDICTION TECHNIQUE - Energy Research Developments: Tidal Energy, Energy Efficiency and Solar Energy - page 358

the least incentive to participate in the balance of wind power. In the future, even nuclear
plants may however need forecasts to be able to give bids on the market and to operate
efficiently.

Table 7. Summary of the cost optimised forecast selection.

Predicted load Competition Forecast Reserve

factor in [%] on regulation choice allocation

0-10 Good on down-regulation EPS minimum Downward

70-100 Good on up-regulation EPS maximum Upward

20-70 Good for up and down Best forecast Down and up

The electricity price has a high volatility level because of the limited storage capacity
and the strong relationship to oil prices, political disputes and not to forget the uncertainty
in the weather development.
An increasing number of people around the world make their living on trading and
because of the automatisation fewer people are required in today's production processes.
This means that in the future, increasing volatility of stocks and energy can and have to be
expected.
However, the volatility of the energy pricing may increase more than that of stocks for
two reasons:

• The amount of intermittent renewables will increase more than the available storage
capacity.

• The energy markets are developing slowly with new trading options.

Increased volatility on pricing will result in increased volatility on the generation as
well, and consequently lower efficiency and higher costs. Increased volatility can also
trigger instabilities on the grid. A typical example could be two competing generators that
have to ramp with opposite sign to stay in balance. Increased volatility implies that the
frequency of ramps will increase. Such ramps are not dangerous, but certainly do not add
to the system security. The generators will bare the loss during the ramp, because of the
higher average price.
The optimisation strategies that we have presented here serve to dampen volatility and
the intermittent energy price. The main ingredients in this optimisation is ensemble fore-
casting, which increases the robustness of the decision process. Decisions will be taken
on the basis of many results that are generated by some kind of perturbation. The market
participants will, with the help of ensemble predictions in the future know in which range
competing parties plan to set their bid on the market. There is also more continuity in
time by using ensemble forecasting, because the decision process changes slowly hour by
hour. This leads to a more stable decision process. Ensemble forecasting makes market
participants aware of the risk of any speculation, although it may not be enough to prevent
speculations.