Historically, electricity has been available whenever it was required. This was achieved by varying the amount of generation to match the demand at all times. This requires very expensive over-provisioning of generation capacity. More importantly, as society makes the transition to sustainable energy sources such as wind and solar, generation can only occur when the resource is available.
Demand response (DR) refers to any system in which the supply-demand balance is achied by deferring certain demands. Typically these demands are from things such as charging batteries or pumping water through swimming pools, which can be done at any time.
This project will investigate important open questions in demand response systems, such as:
There is a growing need for technology to match electricity consumption to non-controllable generation. Energy storage is one such technology.
Storage technology such as batteries and pumped hydro can be used on very short timescales in frequency regulation markets or to provide spinning reserve, or on longer timescales to store solar energy for night time, or store wind energy for cloudy days.
Optimal management of storage is made difficult by imprecise forecasts of future load and future generation. For this reasons, techniques such as Lyapunov optimization have been applied for storage management.
This project will address storage management of elastic storage devices, with imperfections such as inefficiency and self-dischage. In particular, it will look at management of a portfolio of devices with different imperfections, and investigate how much benefit comes from having diversity among storage types.
The power grid used to be centrally managed infrastructure, with a small number of generators whose states could be observed, but a sparsely monitored transmission and distribution network. With the advent of smart meters and distributed generation, we have many more sources of information but also many more states that need to be observed.
This project will explore the use smart meter data for tasks such as:
In order to save electricity, it is important to know where the electricity is going. Putting an electricity meter on every device in a house is expensive, and so there has been considerable interest in estimating the consumption of individual devices based on the measurement of a house's total energy consumption.
Current techniques for this either require a large amount of manual intervention to set up, or are unreliable -- or both. This project will:
Data centres are typically designed to process their peak workload, but workload varies substantially throughout the day. As a result, many people have proposed that some servers be turned off during periods of low load. However, there is a cost for turning servers on and off, and it is not known in advance how long the load will be low for.
This project will investigate popeties of LCP, an on-line algoithm that was ecently poposed to solve this poblem and othe "smoothed online convex optimization" poblems. Specific questions are:
Many systems, such as Microsoft's Azure, spread work among many geographically dispersed data centres. The ease with which data can be transported makes this an opportunity to use data centres as demand-response participants. Load can be shifted to data centres where the availability of renewable energy temporarily exceeds the demand from inflexible sources.
However, rerouting work often requires large databases to be migrated, and so there can be a substantial cost to changing the allocation of work. This makes it hard to determine the optimal routing without knowing future conditions.
This project will explore the use of "metrical task system" algorithms to distribute work between data centres without requiring any future knowledge.
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