System Group lead by Prof Mark O'Malley

Paddy Teahon is introducing the system group lead by Prof Mark O'Malley

Dr Niamh Troy is presenting: Wind Penetration and Power Cycling

Cycling is really important because wind energy has a not deterministic production and also for the market competition.  She modelled a multi-mode operation of combined-cycle Gas turbine.

She use the Wilmar model to allow a multimode function implementing a virtual capacity factor by this combined mode. Then she integrated the increasing wind production.Then she compared the multimode operation with the normal operations of the power plants.

Cycling affect in a positive way the overall performance of the power plants.
These are the result found after three years of test of power plants, we can reach the 30% of saving.

Conclusion:

Increasing wind penetrations shown to increase generator cycling :CCGTs identified in many wind integration studies as being forced out of merit

Modelling various configurations of CCGT benefits the CCGT by allowing a new opportunity to be dispatched and benefits the system through increased flexibility and availability of replacement reserve.

Modelling cycling costs dynamically allows depreciation over time to be represented.

Explicit modelling of cycling costs is shown to reduce cycling operation, thereby reducing cycling costs

Results from test system show modelling dynamic cycling costs alters merit order over time.




Colm Lowery: Uncertainty & Forecast Error Statistics

Schedule Available plant to meet forecasted demand lowest cost/highest reliabilityWind Power adds another stochastic element

It is important to have a scheduling model to:

Optimization model – minimises expected costs (fuel, carbon, startup) subject to load and constraints

It is possible to identify the shape of errors in scheduling through a moment matching.
Setting an Objective Function and some Costraints.

Modelled variance as percentage of true variance

Conclusion:

System behaviour changes with known error information

The type and the accuracy of provided information matters

Forecast error knowledge changes the utilization of different generator categories

Different utilization patterns impacts System Planning

Aonghus Shortt: The Future Grid: Quantifying & Responding to Variability


It is difficult to model the scheduling with the increase on Wind penetration

In the real world the modelling could take from 20 minutes to 1 second with the alghorithm developed  we could reache the throshold of 20 millisenconds.


Quantifying the impact of Wind and compare with the benefit of wind. Negative impact of Wind are the start costs and it doesn't coincide with the demand.
Higher level of wind the plants will not start and so we have to evaluate the 

System benefit Ratio= reduction in total generation cost : increase in total capital costs

With this Ratio we can evaluate the benefit of increasing wind penetration in each country depending also from the correlation between the wind blowing and the actual energy demand.


Evaluation of Electric vehicle impact on the point of view of benefit and costs.


Current Work on Design Long Term Generator Plan: we want to encourage unit with less capital cost.


so what are Compensation Mechanism


Conclusion:

Net value of wind - Variability mitigation through EV -Reform of compensation mechanism