The importance of action in the face of dangerous climate change cannot be understated. With new climate change reports driving home harder than ever the importance of leaving a large quantity of fossil fuel reserves unburnt and in the ground, if our global society is to avoid the worst effects of climate change it is imperative that we swiftly act to replace fossil fuel generation with low-carbon energy sources. Energy storage is a set of (many new) technologies with the potential to facilitate a much higher use of indigenous renewable energy resources, both decreasing greenhouse gas emissions and pollution while at the same time increasing energy security (as well as offering many other benefits to energy systems). However, in the short term the costs of adopting energy storage are likely to be significantly higher than the unabated combustion of fossil fuels (while ignoring the long term consequences); therefore it is crucial that energy policies which encourage this form of sustainable development are put in place. The energy industry of the future will be shaped by the energy policy of today; and the consequences of poor decisions in this regard will not be easily undone. It is only through the coordinated interaction between energy policy and technology that the best solutions will be reached.
In the same vein it is of paramount importance to understand the economics of energy storage, in order to be able to quantify the true costs and rewards of the technology options. This involves understanding the system benefits of energy storage as well as the interactions between energy storage technologies and electricity markets. Electricity markets in particular are complex entities that must ensure the real-time balance between supply and demand, as well as sufficient levels of reliability and redundancy (amongst many other functions). In order to better understand and compare the rewards available to different energy storage technologies, during my PhD we developed a model which compared the arbitrage revenue generating potential of different energy storage technologies. The model considered different characteristics of the technologies including the charging efficiency, discharging efficiency, charging and discharging limits, and time-dependent self-discharge. The inclusion of the self-discharge adds a level of complexity to the optimisation which we opted to solve with a MonteCarlo approach. The work was published in the highly-rated journal Energy & Environmental Science.
Currently I am focussing on further understanding the interactions between energy storage and electricity markets as well as reflecting on the behaviour that electricity markets promote in energy storage devices. This can then be compared to the behaviour that optimises a number of different metrics related to social welfare, for example minimising network emissions or minimising network operational costs.