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Impact of optimal charging of electric vehicles on future generation portfolios
2009-09, Shortt, Aonghus, O'Malley, Mark
Battery electric vehicles are considered by many to be part of a series of measures necessary to reduce global carbon dioxide emissions and dependence on fossil fuel resources. The extent to which this is possible depends on how successfully they can be implemented into the broader system. This paper considers the power systems impact of different vehicle charging regimes. A test system with a high proportion of variable renewables was considered. Charging profiles were developed for slow, fast and controlled optimal charging and optimal generation portfolios were developed using a least-cost optimisation algorithm. It was found that over-night charging at the slow rate resulted in a reduction in the average cost of electricity by between 4.2 and 6% compared to the base-case. For the high charging rate cases, the average cost of electricity rises by between 3 and 7%. When the charging is controlled centrally and optimised so as to increase the minimum system load maximally, it is found that the average cost of electricity is reduced by between 4.5 and 8.2%. None of the above cases resulted in significant changes in the average CO2 emissions per unit electricity output. However, it was found that by increasing the minimum system load, optimal charging could facilitate additional inflexible generation such as variable renewables or nuclear fission plant. Where nuclear capacity is added to the generation portfolios based on optimal charging, average CO2 emissions per unit of electricity are seen to fall between 22 and 41% for the cases studied, with the average cost of electricity reducing by between 9.5 and 21.5%.
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Impact of variable generation in generation resource planning models
2010-07, Shortt, Aonghus, O'Malley, Mark
Long-term power system planning is beset by a trade-off between detail and scope: The chosen approach usually lies somewhere between modeling a great many generation portfolios coarsely and very few in a more detailed manner. This paper seeks to argue that the performance of generation portfolios is inﬂuenced by a sufﬁciently large number of variables, of varying uncertainties, such that the long-term investment problem can only be effectively tackled with very many runs of computationally light models that capture the most essential features of the problem. Taking a linear optimization program as the intended computational core, this paper describes two algorithms to build constraints for the linear program which capture many of the effects that are difficult or impossible to capture directly in non-chronological models, namely: unit starts, unit ramping, unit average output and adequate total system capacity. An application of these methods is also presented.
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Quantifying the long-term power system benefits of electric vehicles
2012-01, Shortt, Aonghus, O'Malley, Mark
The re-emergence of battery electric vehicles presents a potentially vast flexible resource to the power system at a time when renewable generation is pushing the ability of power systems to respond to increased levels of variability in production. Large installed quantities of wind and solar power in certain systems will cause changes in the operation of conventional generators that, over time, will reduce the economic feasibility of cost-effective but inflexible forms of generation. This can be avoided if electric vehicles can be used to mitigate the overall level of system variability by charging at times when production by conventional generators is at its lowest. This paper presents a methodology for determining the degree to which electric vehicles will influence future generation plant investment and a means of calculating the reduction in total system costs owing to the presence of optimally charged electric vehicles. This model is applied to a test system based on the state of Texas. It is found that the long-term benefits of electric vehicles are significant and continue to increase for all vehicle penetration levels studied. However, the relationship between levels of installed variable renewables and electric vehicle benefit was found to be highly dependent on the extent to which both of these parameters influenced the least-cost plant portfolio.