Tokyo Tech scientists have designed a new approach for scheduling the turning on and off of power generators.
According to the scientists, this should minimize costs and ensures reliability of power-distribution networks.
Just like any other machinery, generators have start-up and shut-down times and associated costs.
Because multiple generators are generally available at any given point, they cannot be turned on or off quickly.
Network operators are known to schedule these ‘on’ and ‘off’ operations in advance according to predicted loads in order to reduce these costs.
These schedules are based on mathematical models and strategies that deal with a tradeoff involving minimizing costs and saving energy on the one hand, and ensuring reliability on the other.
One such family of mathematical optimization problems is referred to as ‘unit commitment’ (UC) and has been used to determine the required states (on/off) of generators in power systems.
They aren’t however fool-proof: operators have to deal with uncertainty in multiple variables across the board, such as load, generator availability and failures, and renewable energy input.
Available methods to devise such schedules have several disadvantages. Some of them take the scheduled period as a whole and require taking into account the dynamics of the generators and uncertain variables.
Such uncertainties are usually not properly addressed and this often results in overly conservative schedules being obtained.
Other methods too are unable to deal with energy storage systems, which are essential for renewable energy technologies.
Now Tokyo Tech professor Jun-ichi Imura and his team have developed a novel UC method with an interesting twist to overcome the above mentioned issues.
Instead of taking the scheduled period as a whole and having to deal with system dynamics, the new approach divides the period into time slots to be dealt with individually in real time.
The uncertain values in the model are independently given upper and lower limits for each timeslot, and the interplay of these limits and other constraints are used to derive feasible optimal solutions.
What’s more, the method can be adjusted to consider potential generator outages, the researchers contend.