khalid abdulla, the university of melbourne the value of system aggregation in exploiting renewable...

Khalid Abdulla, The University of Melbourne

The Value of System Aggregation in exploiting Renewable Energy Sources

Professor Saman HalgamugeThe University of Melbourne17th June 2015

Project Co-workers: Khalid Abdulla (PhD student), A/Prof Andrew Wirth and Dr Kent Steer (IBM Research Lab)

Professor Saman Halgamuge, The University of Melbourne

Agenda

1. Brief history of electrical energy supply

2. Distributed solutions for Renewable Energy

3. Value of System Aggregation

4. Forecasting small-scale aggregations of supply & demand

5. Summary


Brief History of Electrical Energy Supply

Brief History

Distributed Solutions

Value of Aggregation

Forecasting

Summary

• 1882: Pearl Street Station, Manhattan. 85 Customers, 400 lamps

1. https://power2switch.com/blog/how-electricity-grew-up-a-brief-history-of-the-electrical-grid/

• 1900: Some centralisation and economies of scale

[1]

[1]



Brief History



Forecasting

Summary

• 1900-1932: Private Electric Companies supply small collections of geographically close customers.

1. https://power2switch.com/blog/how-electricity-grew-up-a-brief-history-of-the-electrical-grid/

[1]

• 1935 onward: Increasing centralisation of electricity generation at greater scales and transmission at higher voltages over longer distances. Typically by state-owned monopolies.

[1]



Brief History



Forecasting

Summary

• ~1980s onwards: Breaking up of generation and transmission businesses, deregulation and establishment of electricity markets in many countries.

• Majority of electricity supply continues to be at large centralised plants thanks to the economies of scale this offers.


Distributed solutions for Renewable Energy

Brief History



Forecasting

Conclusions

• Renewable energy sources (wind, solar) have distributed availability

• Distributed solutions considered to assist their exploitation:• Micro-grids• Distributed energy storage• Embedded generation (roof-top PV, micro wind turbines)

• Researchers have speculated about the “utility death spiral” particularly in places like Australia with good resources and high electricity costs


Distributed solutions for Renewable Energy

Brief History



Forecasting

Conclusions

• If highly distributed electricity supply is the future it raises the possibility of “leap-frogging” by locations yet to be electrified


Value of System Aggregation

Brief History



Forecasting

Conclusions

• However, the benefits of aggregation which drove early grids to larger scales are no less important today:

• Supply and demand diversity reduces capacity requirements

• Economies of scale reduce costs

• Sharing reserves reduces costs and/or improves reliability

• Larger aggregations of demand/RES can be forecast more accurately


Forecasting small-scale aggregations of supply & demand

Brief History



Forecasting

Summary

• To optimally operate an energy supply system we need to be able to forecast supply and demand

• This is more difficult for small-scale aggregations:

[2] R. Sevlian and R. Rajagopal, “Short Term Electricity Load Forecasting on Varying Levels of Aggregation.”

[2]



Brief History



Forecasting

Summary

• In addition the intermittent nature of small-scale demand is such that conventional forecast error metrics produce counter-intuitive results

[3] S. Haben, J. Ward, D. Vukadinovic Greetham, C. Singleton, and P. Grindrod, “A new error measure for forecasts of household-level, high resolution electrical energy consumption,” Int. J. Forecast., vol. 30, no. 2, pp. 246–256, Apr. 2014.

[3]



Brief History



Forecasting

Summary



Brief History



Forecasting

Summary

• We consider ways of producing useful forecasts for small-scale demand aggregations

• The application is to minimise the peak power drawn over a billing period from a set of customers, by charging/discharging a local battery:

Grid

Batt ery

Objective is to minimisepeak power draw n fromgrid



Brief History



Forecasting

Summary

• We train FFNN-based forecast models to minimise various error metrics, and assess the performance of those forecasts in an on-line setting

• We then use the on-line performance (on training data set) to select forecasts for a specific application

Historic DemandData

Train ForecastModel

ForecastModel

Online D em andData

- Model Type- Training Method- Hyperparam eters- Cost Function

Forecast On-lineController

Plant UnderControl



Brief History



Forecasting

Summary

[4] K. Abdulla, K. Steer, A. Wirth, S. Halgamuge, “Forecast error metric selection for online control problems,” IEEE Transactions on Smart Grids (submitted), June 2015.

[4]


Summary

Brief History



Forecasting

Summary

• The distributed availability of RES mean that distributed methods are likely to be an important part of their integration.

• However, the significant benefits offered by system aggregation are as important, perhaps more so, for RES, meaning the lowest overall cost solution is likely to be one with a high degree of interconnection.

• We have begun to quantify one aspect of the system aggregation benefit: the improved forecastability of demand at larger aggregation scales.

• There are many other benefits yet to be fully quantified.

• There is an opportunity for newly-electrified locations to leap-frog to an electricity system based on distributed RES, but if this is to be a lowest cost solution it is likely to still be highly interconnected.

khalid abdulla, the university of melbourne the value of system aggregation in exploiting renewable...

Documents

distributed electricity

university of melbourne

summary slide

majority of electricity

benefits of aggregation

electrified slide

high electricity costs

khalid abdulla phd student