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Energy storage systems are well poised to mitigate uncertainties of renewable generation outputs. Gridscale energy storage projects are major investments which call for rigorous valuation and risk analysis. This paper provides a stochastic energy storage valuation framework in wholesale power markets which considers all key revenue streams simultaneously. As part of this framework, an operational optimization model is developed to determine the energy storage system's optimal dispatch sequences. A future curve model is built to capture the volatilities of electricity prices. In addition, a frequency regulation service price forecasting model is developed. Simulation results with a realistic battery storage system reveal that the majority of the market revenues comes from frequency regulation services. Simulation results also show that both round-trip efficiency and power-to-energy ratio are crucial to the cost effectiveness of energy storage systems.

1 This paper considers the problem of Phase Identification in power distribution systems. In particular, it focuses on improving supervised learning accuracies by focusing on exploiting some of the problem's information theoretic properties. This focus, along with recent advances in Information Theoretic Machine Learning (ITML), helps us to create two new techniques. The first transforms a bound on information losses into a data selection technique. This is important because phase identification data labels are difficult to obtain in practice. The second interprets the properties of distribution systems in the terms of ITML. This allows us to obtain an improvement in the representation learned by any classifier applied to the problem. We tested these two techniques experimentally on real datasets and have found that they yield phenomenal performance in every case. In the most extreme case, they improve phase identification accuracy from 51.7% to 97.3%. Furthermore, since many problems share the physical properties of phase identification exploited in this paper, the techniques can be applied to a wide range of similar problems.

The widespread adoption of battery energy storage systems (BESS) has been hindered by the uncertainty of their financial value. In past research, this value has been estimated by optimizing the system's actions over the course of the battery's lifetime. However, these estimates did not consider the fact that battery actions decrease the lifetime itself. This paper uses realistic battery cycle degradation to re-evaluate BESS profitability and attempts to increase profits by mitigating this degradation. For this purpose, the paper develops an approximate linear model of degradation suitable for co-optimization with the set of battery actions. It is shown through simulation that 29.1% of the storage system's value is lost because of cycle degradation. However, co-optimization through the approximate model reduces this loss to just 3.3%.