1. Khalid Abdulla, The University of Melbourne The Value of System Aggregation in exploiting Renewable Energy Sources Professor Saman Halgamuge The University of Melbourne 17 th June 2015 Project Co-workers: Khalid Abdulla (PhD student), A/Prof Andrew Wirth and Dr Kent Steer (IBM Research Lab)

2. 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

3. Professor Saman Halgamuge, The University of Melbourne 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]

4. Professor Saman Halgamuge, The University of Melbourne Brief History of Electrical Energy Supply Brief History Distributed Solutions Value of Aggregation 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]

5. Professor Saman Halgamuge, The University of Melbourne Brief History of Electrical Energy Supply Brief History Distributed Solutions Value of Aggregation 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.

6. Professor Saman Halgamuge, The University of Melbourne Distributed solutions for Renewable Energy Brief History Distributed Solutions Value of Aggregation 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

7. Professor Saman Halgamuge, The University of Melbourne Distributed solutions for Renewable Energy Brief History Distributed Solutions Value of Aggregation Forecasting Conclusions If highly distributed electricity supply is the future it raises the possibility of “leap-frogging” by locations yet to be electrified

8. Professor Saman Halgamuge, The University of Melbourne Value of System Aggregation Brief History Distributed Solutions Value of Aggregation 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

9. Professor Saman Halgamuge, The University of Melbourne Forecasting small-scale aggregations of supply & demand Brief History Distributed Solutions Value of Aggregation 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]

10. Professor Saman Halgamuge, The University of Melbourne Forecasting small-scale aggregations of supply & demand Brief History Distributed Solutions Value of Aggregation 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]

11. Professor Saman Halgamuge, The University of Melbourne Forecasting small-scale aggregations of supply & demand Brief History Distributed Solutions Value of Aggregation Forecasting Summary

12. Professor Saman Halgamuge, The University of Melbourne Forecasting small-scale aggregations of supply & demand Brief History Distributed Solutions Value of Aggregation 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:

13. Professor Saman Halgamuge, The University of Melbourne Forecasting small-scale aggregations of supply & demand Brief History Distributed Solutions Value of Aggregation 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

14. Professor Saman Halgamuge, The University of Melbourne Forecasting small-scale aggregations of supply & demand Brief History Distributed Solutions Value of Aggregation 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]

15. Professor Saman Halgamuge, The University of Melbourne Summary Brief History Distributed Solutions Value of Aggregation 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.