The increase in renewable energy generators introduced into the electricity grid is putting pressure on its stability and management as predictions of renewable energy sources cannot be accurate or fully controlled. This, with the additional pressure of fluctuations in demand, presents a problem more complex than the current methods of controlling electricity distribution were designed for. A global approximate and distributed optimisation method for power allocation that accommodates uncertainties and volatility is suggested and analysed. It is based on a probabilistic method known as message passing , which has deep links to statistical physics methodology. This principled method of optimisation is based on local calculations and inherently accommodates uncertainties; it is of modest computational complexity and provides good approximate solutions.We consider uncertainty and fluctuations drawn from a Gaussian distribution and incorporate them into the message-passing algorithm. We see the effect that increasing uncertainty has on the transmission cost and how the placement of volatile nodes within a grid, such as renewable generators or consumers, effects it.
|Title of host publication||The proceedings of the 2nd International Conference on Intelligent Green Building and Smart Grid (IGBSG)|
|Number of pages||6|
|Publication status||Published - 11 Aug 2016|
|Event||2nd International Conference on Intelligent Green Building and Smart Grid - Prague, Czech Republic|
Duration: 27 Jun 2016 → 29 Jun 2016
|Conference||2nd International Conference on Intelligent Green Building and Smart Grid|
|Period||27/06/16 → 29/06/16|
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Funding: Alstom; EPSRC (Industrial CASE Studentship 12330048); and Research Grants Council of Hong Kong (grant numbers 604512, 605813).
- message passing
- power flow
- renewable energy
Harrison, E., Saad, D., & Wong, K. Y. M. (2016). Message passing for distributed optimisation of power allocation with renewable resources. In The proceedings of the 2nd International Conference on Intelligent Green Building and Smart Grid (IGBSG) IEEE. https://doi.org/10.1109/IGBSG.2016.7539451