This question, although not covered here in the general context of society's challenges and issues of cooperation in companies, has found a new application.
The energy sector faces major challenges due to the realisation of the Energiewende [transition to renewable energies] in Germany. As before, the question of what should be implemented for it to succeed remains and, once this has been answered, the question is how should it be implemented. All these questions often bring about the distorted view that the Energiewende produces more questions than answers overall; this clouds the view of what has already been achieved. Due to the obligation to sell electricity from wind, photovoltaic and biogas plants in the German Renewable Energy Sources Act [EEG] by way of 'direct marketing', a new market has developed with a large number of participants in a market place which had formerly been used by just a handful of companies. This is a market that never sleeps and where computers increasingly make trades – EPEXSpot, the continuous intraday power exchange in Paris.
This is where companies active in direct marketing meet the operators of conventional power stations. Due to the timing of marketing on the exchange at 12:00 for all hours of the following day, the forecasts used when generating electricity are at least twelve hours old. As market participants are obligated to ensure that the feed-in corresponds exactly to the volumes sold, positions are then adjusted in continuous intraday trading so that, as far as possible, electricity production does not need to be adjusted. An adjustment can only occur through a reduction in the feed-in, which would entail opportunity costs.
Everyone knows from daily experience how imprecise weather forecasts are, how you only see clouds instead of the sun. Predictions for wind strength and storm warnings, etc., most often do not occur as forecast. In the electricity market, forecasts must fit exactly for every quarter-hour of the day of consumption, meaning that there are several forecast updates per hour for feed-in from wind and photovoltaic, which then must be traded.
For example, if wind and sunlight forecasts predict that 40,000 MW must be sold, i.e. less than half of the installed capacity of > 80,000 in direct marketing, and this was implemented in sales transactions on the prior day, a 10% reduction in the forecast would lead to a sudden spike in demand for power plant capacity of 4,000 MW, i.e. more than six blocks of a hard-coal-fired power plant. It is understandable that this short-term demand causes a price hike.
Prices on the electricity market are set at 12:00 on the prior day and reflect the expected feed-in from renewable energy plants. High feed-in leads to a lower electricity price, while low feed-in leads in turn to a high electricity price. If a shift in wind gusting is now forecast, this has two effects:
To be able to be active in this market, participants must be able to demonstrate flexibility in procuring and generating electricity. Additional significant potential in unused flexibility is assumed to exist, especially in the industrial sector. At issue here is a 'buffer', which allows electricity procurement to be delayed or allows for types of use for which electricity procurement can temporarily be slightly reduced or, if necessary, increased.
A very precise analysis of the production processes, e.g. in basic industry, in the use of air pressure or of heat and cold storage, etc., connected to the power procurement agreement, can reveal what potential exists. The caveat is that the production process remains the most important factor. Through the availability of as-is forecast data within the process, however, today there are increasingly better ways of identifying potential to delay procurement. Through an automated optimisation solution, this can then be monetised directly or via a supplier on the energy market.
By achieving and marketing flexibility, profit can be generated for the company and something good can also be done at the same time: supporting the success of the Energiewende; you won't find a combination like that so often these days.
A basic precondition for exploiting this earnings potential is of course high-performance IT systems that can process real-time forecast data and also translate this data into automated buy and sell orders. We are thus seeing a development on the energy market that was established a long time ago on volatile and liquid financial markets – the rise of 'algo trading'. And as with financial markets, the highest returns are on offer to pioneers with sophisticated technology.
Source: KPMG Corporate Treasury News, Edition 65, March 2017
Author: Malte Neuendorff, Senior Manager, Finance Advisory
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