Publications

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Reliable engineering quality, safety, and performance are essential for a successful energy-storage project. The commercial energy-storage industry is entering its most formative period, which will impact the arc of the industry's development for years to come. Project announcements are increasing in both frequency and scale. Energy-storage systems (ESSs) are establishing themselves as a viable option for deployment across the entire electricity infrastructure as grid-connected energy-storage assets or in combination with other grid assets, such as hybrid generators. In conclusion, how the industry will evolve-in direction and degree-will depend largely on building a firm foundation of sound engineering requirements into project expectations.

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Energy storage is a unique grid asset in that it is capable of providing a number of grid services. In market areas, these grid services are only as valuable as the market prices for the services provided. This paper formulates the optimization problem for maximizing energy storage revenue from arbitrage (day-ahead and real-time markets) in the California Independent System Operator (CAISO) market. The optimization algorithm was then applied to three years of historical market data (2014-2016) at 2200 nodes to quantify the locational and time-varying nature of potential revenue. The optimization assumed perfect foresight, so it provides an upper bound on the maximum expected revenue. Since California is starting to experience negative locational marginal prices (LMPs) because of increased renewable generation, the optimization includes a duty cycle constraint to handle negative LMPs. Two additional trading algorithms were tested that do not require perfect foresight. The first sets a buy price threshold and a sell price threshold (e.g., limit orders) for participation in the real time market, subject to the constraints of the energy storage system. The second uses the day-ahead prices as an estimate for the real time prices and performs an optimization on a rolling time horizon. The simple threshold algorithm performed the best, but both fell well short of the potential revenue identified by the optimization with perfect foresight.

Techno-economic analyses of energy storage currently use constant-efficiency energy flow models. In practice, charge/discharge efficiency of energy storage varies as a function of state-of-charge, temperature, charge/discharge power. Therefore, using the constant-efficiency energy flow models will cause suboptimal results. This work focuses on incorporating nonlinear energy flow models based on nonlinear efficiency models in the revenue maximization problem of energy storage. Dynamic programming is used to solve the optimization problem. A case studies is conducted to maximize the revenue of a Vanadium Redox Flow Battery (VRFB) system in PJM's energy and frequency regulation market.

The increased penetration of renewable resources has made frequency regulation and generation control a growing concern. This has created an opportunity for Energy Storage Resource to participate in the frequency regulation market. This paper investigates the potential of Battery Energy Storage systems to participate in the German secondary frequency regulation market. A simulation model is developed to investigate the revenue opportunity of a 48 MWh Battery System participating in the secondary frequency regulation market.

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