Key Technologies

The trading and dispatch prices in the electricity market are subject not only to the system demand and supply availability but in a very significant way to the bid and offers of participant generators, retailers and consumers.   These bids and offers are not static.   Traders respond to the changing market conditions by changing their bid and offer to the pool at that same time.   The iPool software is equipped with a fuzzy inference system (also known as fuzzy logic) which models the bidding behavior such that bids respond in accordance with the kind and degree of change in the market condition during simulation.   For example, if the energy storage goes below a certain level, the bid offers of the pump storage plant can adjust accordingly to preserve the stored energy for times where it is most beneficial to use.   Another example is when a station unit goes on outage, the bid offers of the remaining station units in a portfolio can change proportionately to compensate for the lost capacity in order to fulfill a contracted capacity requirement.   

Monte Carlo is a mathematical technique that employs repeated simulation of a stochastic system in order to arrive at a statistically valid result.   The  nature of the operation of an electricity market and a power system are probabilistic.   Generator units break down and the timing and duration of their outages, unless they were planned, are random.   The randomness follow a particular probability distribution curve called the Weibull curve which is similar to an exponential distribution.   These supply outages affect the spot prices significantly and they are often the cause of extreme price jumps.   It is of prime importance in forecasting and planning especially in the long term to be able to quantify the effect of these random supply outage events.   The iPool software models these supply outages and other random events in a way that is close to reality – that is in a chronological sequential manner using mean time to fail and repair availability parameters of each generating unit in the system that iPool can also determine from historical data.   It simulates each dispatch interval of a year in consideration of the random outage supply events and other events.  It does the full year simulation repeatedly and each time with a different set of random events.   This chronological sequential way of Monte Carlo simulation provides the most accurate method of forecasting prices as it is able to model the extreme price jumps which represent the tail end part of the price duration curve.   Not all Monte Carlo techniques used in the power industry are the same.   Non sequential non chronological implementation are faster but they are not able to model the extreme price jumps and the corresponding tail end of the price duration curve which are highly important in planning and forecasting. 

If you have any questions or want to discuss a project, feel free to contact iEnergy Aust Pty Ltd. We are always happy to hear from potential clients and provide a product presentation.