Publications

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This paper explores the revenue potential for electric storage resources (ESRs), also referred to as electrical energy storage, in the Southwest Power Pool Integrated Marketplace. In particular, opportunities in the day-ahead market with the energy and frequency regulation products are considered. The revenue maximization problem is formulated as a linear program model, where an ESR seeks to maximize its revenue through the available revenue streams. The ESR has perfect foresight of historical prices and determines the optimal policy accordingly. A case study using FY2018 data shows that frequency regulation services are the most lucrative for revenue potential. This paper also explores different methods of using area control error data to infer the regulation control signal and the consequent effect on the optimization. Finally, the paper conducts a sensitivity analysis of ESR payback period to energy capacity and power rating.

Included in the 1-page brief are sections on project purpose, technical approach, 2019 plans and expectations, and references.

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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.

This report presents a complete listing, as of May 2019, of the damping controller (DCON) project accomplishments including a project overview, project innovations, awards, patent application, journal papers, conference papers, project reports, and project presentations. The purpose of the DCON is to mitigate inter-area oscillations in the WI by active improvement of oscillatory mode damping using phasor measurement unit (PMU) feedback to modulate power flow in the PDCI. The DCON project is the result of a collaboration between Sandia National Laboratories (SNL), Montana Technological University (MTU), Bonneville Power Administration (BPA), and the Department of Energy Office of Electricity (DOE-OE).

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As batteries become more prevalent in grid energy storage applications, the controllers that decide when to charge and discharge become critical to maximizing their utilization. Controller design for these applications is based on models that mathematically represent the physical dynamics and constraints of batteries. Unrepresented dynamics in these models can lead to suboptimal control. Our goal is to examine the state-of-the-art with respect to the models used in optimal control of battery energy storage systems (BESSs). This review helps engineers navigate the range of available design choices and helps researchers by identifying gaps in the state-of-the-art. BESS models can be classified by physical domain: state-of-charge (SoC), temperature, and degradation. SoC models can be further classified by the units they use to define capacity: electrical energy, electrical charge, and chemical concentration. Most energy based SoC models are linear, with variations in ways of representing efficiency and the limits on power. The charge based SoC models include many variations of equivalent circuits for predicting battery string voltage. SoC models based on chemical concentrations use material properties and physical parameters in the cell design to predict battery voltage and charge capacity. Temperature is modeled through a combination of heat generation and heat transfer. Heat is generated through changes in entropy, overpotential losses, and resistive heating. Heat is transferred through conduction, radiation, and convection. Variations in thermal models are based on which generation and transfer mechanisms are represented and the number and physical significance of finite elements in the model. Modeling battery degradation can be done empirically or based on underlying physical mechanisms. Empirical stress factor models isolate the impacts of time, current, SoC, temperature, and depth-of-discharge (DoD) on battery state-of-health (SoH). Through a few simplifying assumptions, these stress factors can be represented using regularization norms. Physical degradation models can further be classified into models of side-reactions and those of material fatigue. This article demonstrates the importance of model selection to optimal control by providing several example controller designs. Simpler models may overestimate or underestimate the capabilities of the battery system. Adding details can improve accuracy at the expense of model complexity, and computation time. Our analysis identifies six gaps: deficiency of real-world data in control literature, lack of understanding in how to balance modeling detail with the number of representative cells, underdeveloped model uncertainty based risk-averse and robust control of BESS, underdevelopment of nonlinear energy based SoC models, lack of hysteresis in voltage models used for control, lack of entropy heating and cooling in thermal modeling, and deficiency of knowledge in what combination of empirical degradation stress factors is most accurate. These gaps are opportunities for future research.

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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.

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.

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.