A mixed-integer model for optimal grid-scale energy storage allocation
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To meet ambitious upcoming state renewable portfolio standards (RPSs), respond to customer demand for “green” electricity choices and to move towards more renewable, domestic and clean sources of energy, many utilities and power producers are accelerating deployment of wind, solar photovoltaic and solar thermal generating facilities. These sources of electricity, particularly wind power, are highly variable and difficult to forecast. To manage this variability, utilities can increase availability of fossil fuel-dependent backup generation, but this approach will eliminate some of the emissions benefits associated with renewable energy. Alternately, energy storage could provide needed ancillary services for renewables. Energy storage could also support other operational needs for utilities, providing greater system resiliency, zero emission ancillary services for other generators, faster responses than current backup generation and lower marginal costs than some fossil fueled alternatives. These benefits might justify the high capital cost associated with energy storage. Quantitative analysis of the role energy storage can have in improving economic dispatch, however, is limited. To examine the potential benefits of energy storage availability, a generalized unit commitment model of thermal generating units and energy storage facilities is developed. Initial study will focus on the city of Austin, Texas. While Austin Energy’s proximity to and collaborative partnerships with The University of Texas at Austin facilitated collaboration, their ambitious goal to produce 30-35% of their power from renewable sources by 2020, as well as their continued leadership in smart grid technology implementation makes them an excellent initial test case. The model developed here will be sufficiently flexible that it can be used to study other utilities or coherent regions. Results from the energy storage deployment scenarios studied here show that if all costs are ignored, large quantities of seasonal storage are preferred, enabling storage of plentiful wind generation during winter months to be dispatched during high cost peak periods in the summer. Such an arrangement can yield as much as $94 million in yearly operational cost savings, but might cost hundreds of billions to implement. Conversely, yearly cost reductions of $40 million can be achieved with one CAES facility and a small fleet of electrochemical storage devices. These results indicate that small quantities of storage could have significant operational benefit, as they manage only the highest cost hours of the year, avoiding the most expensive generators while improving utilization of renewable generation throughout the year. Further study using a modified unit commitment model can help to narrow the performance requirements of storage, clarify optimal storage portfolios and determine the optimal siting of this storage within the grid.