Publications

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A generic constant-efficiency energy flow model is commonly used in techno-economic analyses of grid energy storage systems. In practice, charge and discharge efficiencies of energy storage systems depend on state of charge, temperature, and charge/discharge powers. Furthermore, the operating characteristics of energy storage devices are technology specific. Therefore, generic constant-efficiency energy flow models do not accurately capture the system performance. In this work, we propose to use technology-specific nonlinear energy flow models based on nonlinear operating characteristics of the storage devices. These models are incorporated into an optimization problem to find the optimal market participation of energy storage systems. We develop a dynamic programming method to solve the optimization problem and perform two case studies for maximizing the revenue of a vanadium redox flow battery (VRFB) and a Li-ion battery system in Pennsylvania New Jersey Maryland (PJM) interconnection's energy and frequency regulation markets.

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Different Federal Energy Regulator Commission (FERC) orders have provided the opportunity for battery energy storage systems (ESSs) to participate in markets. The ability to be a fast-ramping generator or load allows ESSs to provide different grid services. This paper discusses opportunities for ESSs to participate in multiple existing and future electricity markets. The economic value of ESSs can be further increased by pragmatically participating in markets and services considering operational and degradation aspects. The impact of ESS on grid resilience is discussed, including resilience-as-a -service. ESSs can restore the grid to its 100% resilient state during system events, and may also reduce the resilience degradation time during extreme events.

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Energy storage systems are flexible and controllable resources that can provide a number of services for the electric power grid. Many technologies are available, and corresponding models vary greatly in level of detail and tractability. In this work, we propose an adaptive optimal control and estimation approach for real-time dispatch of energy storage systems that neither requires accurate state-of-energy measurements nor knowledge of an accurate state-of-energy model. Specifically, we formulate an online optimization problem that simultaneously solves moving horizon estimation and model predictive control problems, which results in estimates of the state-of-energy, estimates of the charging and discharging efficiencies, and future dispatch signals. We present a numerical example in which the plant is a nonlinear, time-varying Lithium-ion battery model and show that our approach effectively estimates the state-of-energy and dispatches the system without accurate knowledge of the dynamics and in the presence of significant measurement noise.

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Energy storage systems are flexible resources that accommodate and mitigate variability and uncertainty in the load and generation of modern power systems. We present a stochastic optimization approach for sizing and scheduling an energy storage system (ESS) for behind-the-meter use. Specifi-cally, we investigate the use of an ESS with a solar photovoltaic (PV) system and a generator in islanded operation tasked with balancing a critical load. The load and PV generation are uncertain and variable, so forecasts of these variables are used to determine the required energy capacity of the ESS as well as the schedule for operating the ESS and the generator. When the forecasting uncertainties can be fit to normal distributions, the probabilistic load balancing constraint can be reformulated as a linear inequality constraint, and the resulting optimization problem can be solved as a linear program. Finally, we present results from a case study considering the balancing of the critical load of a water treatment plant in islanded operation.

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 and frequency regulation in the 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. The results show that participating in frequency regulation provides approximately 3.4 times the revenue of arbitrage. In addition, arbitrage potential revenue is highly location-specific. Since there are only a handful of zones for frequency regulation, the distribution of potential revenue from frequency regulation is much tighter.

In this work, we provide an economic analysis of using behind-the-meter (BTM) energy storage systems (ESS) for time-of-use (TOU) bill management together with power factor correction. A nonlinear optimization problem is formulated to find the optimal ESS's charge/discharge operating scheme that minimizes the energy and demand charges while correcting the power factor of the utility customers. The energy storage's state of charge (SOC) and inverter's power factor (PF) are considered in the constraints of the optimization. The problem is then transformed to a Linear Programming (LP) problem and formulated using Pyomo optimization modeling language. Case studies are conducted for a waste water treatment plant (WWTP) in New Mexico.

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