Publications

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In this work, we introduce the concept of virtual transmission using large-scale energy storage systems. We also develop an optimization framework to maximize the monetized benefits of energy storage providing virtual transmission in wholesale markets. These benefits often come from relieving congestion for a transmission line, including both reduction in energy cost for the downstream loads and increase in production revenue for the upstream generators of the congested line. A case study is conducted using ISO-New England data to demonstrate the framework.

In a power distribution network with energy storage systems (ESS) and advanced controls, traditional monitoring and protection schemes are not well suited for detecting anomalies such as malfunction of controllable devices. In this work, we propose a data-driven technique for the detection of incidents relevant to the operation of ESS in distribution grids. This approach leverages the causal relationship observed among sensor data streams, and does not require prior knowledge of the system model or parameters. Our methodology includes a data augmentation step which allows for the detection of incidents even when sensing is scarce. The effectiveness of our technique is illustrated through case studies which consider active power dispatch and reactive power control of ESS.

Energy storage systems (ESSs) are being deployed widely due to numerous benefits including operational flexibility, high ramping capability, and decreasing costs. This study investigates the economic benefits provided by battery ESSs when they are deployed for market-related applications, considering the battery degradation cost. A comprehensive investment planning framework is presented, which estimates the maximum revenue that the ESS can generate over its lifetime and provides the necessary tools to investors for aiding the decision making process regarding an ESS project. The applications chosen for this study are energy arbitrage and frequency regulation. Lithium-ion batteries are considered due to their wide popularity arising from high efficiency, high energy density, and declining costs. A new degradation cost model based on energy throughput and cycle count is developed for Lithium-ion batteries participating in electricity markets. The lifetime revenue of ESS is calculated considering battery degradation and a cost-benefit analysis is performed to provide investors with an estimate of the net present value, return on investment and payback period. The effect of considering the degradation cost on the estimated revenue is also studied. The proposed approach is demonstrated on the IEEE Reliability Test System and historical data from PJM Interconnection.

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Large-scale integration of energy storage on the electric grid will be essential to enabling greater penetration of intermittent renewable energy sources, modernizing the grid for increased flexibility security, reliability, and resilience, and enabling cleaner forms of transportation. The purpose of this report is to summarize Sandia's research and capabilities in energy storage and to provide a preliminary roadmap for future efforts in this area that can address the ongoing program needs of DOE and the nation. Mission and vision statements are first presented followed by an overview of the organizational structure at Sandia that provides support and activities in energy storage. Then, a summary of Sandia's energy storage capabilities is presented by technology, including battery storage and materials, power conversion and electronics, subsurface-based energy storage, thermal/thermochemical energy storage, hydrogen storage, data analytics/systems optimization/controls, safety of energy storage systems, and testing/demonstrations/model validation. A summary of identified gaps and needs is also presented for each technology and capability.

The state of California is leading the nation with respect to solar energy and storage. The California Energy Commission has mandated that starting in 2020 all new homes must be solar powered. In 2010 the California state legislature adopted an energy storage mandate AB 2514. This required California's three largest utilities to contract for an additiona11.3 GW of energy storage by 2020, coming online by 2024. Therefore, there is keen interest in the potential advantages of deploying solar combined with energy storage. This paper formulates the optimization problem to identify the maximum potential revenue from pairing storage with solar and participating in the California Independent System Operator (CAISO) day ahead market for energy. Using the optimization formulation, five years of historical market data (2014-2018) for 2, 172 price nodes were analyzed to identify trends and opportunities for the deployment of solar plus storage.

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Increased deployment of rooftop solar in California has resulted in the "duck curve", where there is a decrease in the midday net load followed by a very fast increase in late afternoon caused by declining solar output and a simultaneous increase in load. The large observed and even larger predicted ramp rates present a reliability concern. Therefore, there is keen interest in the potential advantages of deploying solar combined with energy storage. This paper formulates the optimization problem to identify the maximum potential revenue from pairing storage with solar and participating in the California Independent System Operator (CAISO) hour-ahead scheduling process (HASP) real-time market for energy. Using the optimization formulation, five years of historical market data (2014-2018) for 2, 172 price nodes was analyzed to identify trends and opportunities for the deployment of solar plus storage. In addition, a comparison of the opportunities in the day ahead and HASP real-time energy markets is presented.

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This paper presents a techno-economic analysis of behind-the-meter (BTM) solar photovoltaic (PV) and battery energy storage systems (BESS) applied to an Electric Vehicle (EV) fast-charging station. The goal is to estimate the maximum return on investment (ROI) that can be obtained for optimum BESS and PV size and their operation. Fast charging is a technology that will speed up mass adoption of EVs, which currently requires several hours to achieve full recharge in level 1 or 2 chargers. Fast chargers demand from tens to hundreds of kilowatts from the distribution grid, potentially leading to system congestion and overload. The problem is formulated as a linear program that obtains the size of PV, power and energy ratings of BESS as well as charging and discharging scheduling of the storage system to maximize ROI under operational constraints of BESS and PV. The revenue are cost-savings of demand and time-of-use charges, with a penalty for BESS degradation. We have considered Los Angeles Department of Water and Power tariff A-2 and fast charger data derived from the EV Project. The results show that a 46.5 kW/28.3 kWh BESS can obtain a ROI of about $22.4k over 10 years for a small 4-port fast-charging station.

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This paper presents a techno-economic analysis of behind-the-meter (BTM) solar photovoltaic (PV) and battery energy storage systems (BESS) applied to an Electric Vehicle (EV) fast-charging station. The goal is to estimate the maximum return on investment (ROI) that can be obtained for optimum BESS and PV size and their operation. Fast charging is a technology that will speed up mass adoption of EVs, which currently requires several hours to achieve full recharge in level 1 or 2 chargers. Fast chargers demand from tens to hundreds of kilowatts from the distribution grid, potentially leading to system congestion and overload. The problem is formulated as a linear program that obtains the size of PV, power and energy ratings of BESS as well as charging and discharging scheduling of the storage system to maximize ROI under operational constraints of BESS and PV. The revenue are cost-savings of demand and time-of-use charges, with a penalty for BESS degradation. We have considered Los Angeles Department of Water and Power tariff A-2 and fast charger data derived from the EV Project. The results show that a 46.5 kW/28.3 kWh BESS can obtain a ROI of about $22.4k over 10 years for a small 4-port fast-charging station.

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Low frequency electromechanical oscillations can pose a threat to the stability of power systems if not properly addressed. This paper proposes a novel methodology to damp these inter-area oscillations using loads, the demand side of the system. In the proposed methodology, loads are assigned to an aggregated cluster whose demand is modulated for oscillation damping. The load cluster control action is obtained from an optimal output feedback control (OOFC) strategy. The paper presents an extension to the regular OOFC formulation by imposing a constraint on the sum of the rows in the optimal gain matrix. This constraint is useful when the feedback signals are generator speeds. In this case, the sum of the rows of the optimal gain matrix is the droop gain of each load actuator. Time-domain simulations of a large-scale power system are used to demonstrate the efficacy of the proposed control algorithm. Two different cases are considered: a power imbalance and a line fault. The simulation results show that the proposed controllers successfully damp inter-area oscillations under different operating conditions and with different clustering for the events considered. In addition, the simulations illustrate the benefit of the proposed extension to the OOFC that enable load to provide a combination of droop control and small signal stability augmentation.