Efficient operation of battery energy storage systems requires that battery temperature remains within a specific range. Current techno-economic models neglect the parasitic loads heating and cooling operations have on these devices, assuming they operate at constant temperature. In this work, these effects are investigated considering the optimal sizing of battery energy storage systems when deployed in cold environments. A peak shaving application is presented as a linear programming problem which is then formulated in the PYOMO optimization programming language. The building energy simulation software EnergyPlus is used to model the heating, ventilation, and air conditioning load of the battery energy storage system enclosure. Case studies are conducted for eight locations in the United States considering a nickel manganese cobalt oxide lithium ion battery type and whether the power conversion system is inside or outside the enclosure. The results show an increase of 42% to 300% in energy capacity size, 43% to 217% in power rating, and 43% to 296% increase in capital cost dependent on location. This analysis shows that the heating, ventilation, and air conditioning load can have a large impact on the optimal sizes and cost of a battery energy storage system and merit consideration in techno-economic studies.
The knowledge of long-term health and reliability of energy storage systems is still unknown, yet these systems are proliferating and are expected increasingly to assist in the maintenance of grid reliability. Understanding long-term reliability and performance characteristics to the degree of knowledge similar to that of traditional utility assets requires operational data. This guideline is intended to inform numerous stakeholders on what data are needed for given functions, how to prescribe access to those data and the considerations impacting data architecture design, as well as provide these stakeholders insight into the data and data systems necessary to ensure storage can meet growing expectations in a safe and cost-efficient manner. Understanding data needs, the systems required, relevant standards, and user needs early in a project conception aids greatly in ensuring that a project ultimately performs to expectations.
Electrical safety guidance in NFPA 70E for work on batteries can be substantially improved. Article 120, Establishing an Electrically Safe Work Condition was originally developed to manage electrical sources that can be de-energized, e.g., facility ac/dc power circuits. Some have inappropriately attempted to apply electrical safety practices intended for power distribution circuits to battery work. This includes attempts to de-energize batteries, verify zero energy, or establish an electrically safe work condition, none of which can be applied to batteries. However, the principles of the control of hazardous energy, including lockout tagout, can and need to be adapted to work on batteries. This paper explores the modifications required to develop a battery hazardous energy control procedure that can protect workers and avoid accidents. The paper also covers several physical properties and engineering controls common in battery systems that affect the battery risk assessment required by NFPA 70E. Lastly, the paper presents a list of changes proposed to electrical safety practices, including those outlined in NFPA 70E, that clarify how to control hazardous energy in batteries, helping to avoid future misapplication of power distribution circuit electrical safety practices to batteries.
The Waste Isolation Pilot Plant (WIPP) is an underground facility designed to safely dispose of radioactive waste. The WIPP uses many heavy vehicles to transport materials and equipment underground. Most of these vehicles are powered by traditional internal combustion engines (ICE) with diesel fuel. Recently, electric vehicles (EVs) powered with batteries have been used at the WIPP. EVs have very low operational and maintenance costs, not considering battery replacements, and they have zero emissions during operation. This absence of emissions makes them ideal for underground facilities with limited ventilation. Even if a facility has robust ventilation normally, ventilation systems can break down leading to restrictions in ICE powered operations. Figure 1 shows a rendering of the WIPP.
Energy storage technologies are positioned to play a substantial role in power delivery systems. They have the potential to serve as an effective new resource to maintain reliability and allow for increased penetration of renewable energy. However, because of their relative infancy, there is a lack of knowledge about how these resources truly operate over time. A data analysis can help ascertain the operational and performance characteristics of these emerging technologies. Rigorous testing and a data analysis are important for all stakeholders to ensure a safe, reliable system that performs predictably on a macro level. Standardizing testing and analysis approaches to verify the performance of energy storage devices, equipment, and systems when integrating them into the grid will improve the understanding and benefit of energy storage over time from technical and economic vantage points. Demonstrating the life-cycle value and capabilities of energy storage systems begins with the data that the provider supplies for the analysis. After a review of energy storage data received from several providers, some of these data have clearly shown to be inconsistent and incomplete, raising the question of their efficacy for a robust analysis. This report reviews and proposes general guidelines, such as sampling rates and data points, that providers must supply for a robust data analysis to take place. Consistent guidelines are the basis of a proper protocol and ensuing standards to (1) reduce the time that it takes for data to reach those who are providing the analysis; (2) allow them to better understand the energy storage installations; and (3) enable them to provide a high-quality analysis of the installations. The report is intended to serve as a starting point for what data points should be provided when monitoring. Readers are encouraged to use the guidance in the report to develop specifications for new systems, as well as enhance current efforts to ensure optimal storage performance. As battery technologies continue to advance and the industry expands, the report will be updated to remain current.
Battery based energy storage systems are becoming a critical part of a modernized, resilient power system. However, batteries have a unique combination of hazards that can make design and engineering of battery systems difficult. This report presents a systematic hazard analysis of a hypothetical, grid scale lithium-ion battery powerplant to produce sociotechnical "design objectives" for system safety. We applied system's theoretic process analysis (STPA) for the hazard analysis which is broken into four steps: purpose definition, modeling the safety control structure, identifying unsafe control actions, and identifying loss scenarios. The purpose of the analysis was defined as to prevent event outcomes that can result in loss of battery assets due to fires and explosions, loss of health or life due to battery fires and explosions, and loss of energy storage services due to non- operational battery assets. The STPA analysis resulted in identification of six loss scenarios, and their constituent unsafe control actions, which were used to define a series of design objectives that can be applied to reduce the likelihood and severity of thermal events in battery systems. These design objectives, in all or any subset, can be utilized by utilities and other industry stakeholders as "design requirements" in their storage request for proposals (RFPs) and for evaluation of proposals. Further, these design objectives can help to protect firefighters and bring a system back to full functionality after a thermal event. We also comment on the hazards of flow battery technologies.
Arc flash hazard prediction methods have become more sophisticated because the knowledge about arc flash phenomenon has advanced since the publication of IEEE Std. 1584-2002 [17]. The IEEE Std. 1584-2018 [13] has added parameters for more accurate arc flash incident energy, arcing current and protection boundary estimation. The parameters in the updated estimation models include electrode configuration, open circuit voltage, bolted fault current, arc duration, gap width, working distance, and enclosure dimension. The sensitivity and effect changes of other parameters have been discussed the previous literatures [8] [9] [11] [2] [12] [15], this paper explains the fundamental theory on the selection of electrode configurations and performs sensitivity analysis of the enclosure dimension, that have been introduced in the IEEE Std. 1584-2018. According to the newly published model for incident energy (IE) estimation, the IE between VCB (Vertical Electrodes inside a metal Box) and HCB (Horizontal Electrodes inside a metal Box) can differ by a factor of two with other parameters constant. Using HCB as the worst-case scenario to determine the personal protection requirements [7] [10] may not be the best practice in all circumstances. This paper provides guidance for electrode configuration selection and a sensitivity analysis for determining a reasonable engineering margin when actual dimension is not available.
Battery energy storage systems are often controlled through an energy management system (EMS), which may not have access to detailed models developed by battery manu-facturers. The EMS contains a model of the battery system's performance capabilities that enables it to optimize charge and discharge decisions. In this paper, we develop a process for the EMS to calculate and improve the accuracy of its control model using the operational data produced by the battery system. This process checks for data salience and quality, identifies candidate parameters, and then calculates their accuracy. The process then updates its model of the battery based on the candidate parameters and their accuracy. We use a charge reservoir model with a first order equivalent circuit to represent the battery and a flexible open-circuit-voltage function. The process is applied to one year of operational data from two lithium-ion batteries in a battery system located in Sterling, MA USA. Results show that the process quickly learns the optimal model parameters and significantly reduces modeling uncertainty. Applying this process to an EMS can improve control performance and enable risk-averse control by accounting for variations in capacity and efficiency.
When batteries supply behind-the-meter services such as arbitrage or peak load management, an optimal controller can be designed to minimize the total electric bill. The limitations of the batteries, such as on voltage or state-of-charge, are represented in the model used to forecast the system's state dynamics. Control model inaccuracy can lead to an optimistic shortfall, where the achievable schedule will be costlier than the schedule derived using the model. To improve control performance and avoid optimistic shortfall, we develop a novel methodology for high performance, risk-averse battery energy storage controller design. Our method is based on two contributions. First, the application of a more accurate, but non-convex, battery system model is enabled by calculating upper and lower bounds on the globally optimal control solution. Second, the battery model is then modified to consistently underestimate capacity by a statistically selected margin, thereby hedging its control decisions against normal variations in battery system performance. The proposed model predictive controller, developed using this methodology, performs better and is more robust than the state-of-the-art approach, achieving lower bills for energy customers and being less susceptible to optimistic shortfall.
Energy storage technologies are positioned to play a substantial role in power delivery systems. They are being touted as an effective new resource to maintain reliability and allow for increased penetration of renewable energy. However, due to their relative infancy, there is a lack of knowledge on how these resources truly operate over time. Data analysis can help ascertain the operational and performance characteristics of these emerging technologies. Rigorous testing and data analysis are important for all stakeholders to ensure a safe, reliable system that performs predictably on a macro level. Standardizing testing and analysis approaches to verifying the performance of energy storage devices, equipment, and systems when integrating them into the grid will improve the understanding and benefit of energy storage over time from technical and economic vantage points. Demonstrating the life-cycle value and capabilities of energy storage systems begins with the data the provider supplies for analysis. After review of energy storage data received from several providers, it has become clear that some of these data are inconsistent and incomplete, raising the question of their efficacy for robust analysis. This report reviews and proposes general guidelines such as sampling rates and data points that providers must supply for robust data analysis to take place. Consistent guidelines are the basis of the proper protocol and ensuing standards to (a) reduce the time it takes data to reach those who are providing analysis; (b) allow them to better understand the energy storage installations; and (c) enable them to provide high-quality analysis of the installations. This report is intended to serve as a starting point for what data points should be provided when monitoring. As battery technologies continue to advance and the industry expands, this report will be updated to remain current.
Battery energy storage is being installed behind-the-meter to reduce electrical bills while improving power system efficiency and resiliency. This paper demonstrates the development and application of an advanced optimal control method for battery energy storage systems to maximize these benefits. We combine methods for accurately modeling the state-of-charge, temperature, and state-of-health of lithium-ion battery cells into a model predictive controller to optimally schedule charge/discharge, air-conditioning, and forced air convection power to shift a electric customer's consumption and hence reduce their electric bill. While linear state-of-health models produce linear relationships between battery usage and degradation, a non-linear, stress-factor model accounts for the compounding improvements in lifetime that can be achieved by reducing several stress factors at once. Applying this controller to a simulated system shows significant benefits from cooling-in-the-loop control and that relatively small sacrifices in bill reduction performance can yield large increases in battery life. This trade-off function is highly dependent on the battery's degradation mechanisms and what model is used to represent them.
Battery energy storage is being installed behind-the-meter to reduce electrical bills while improving power system efficiency and resiliency. This paper demonstrates the development and application of an advanced optimal control method for battery energy storage systems to maximize these benefits. We combine methods for accurately modeling the state-of-charge, temperature, and state-of-health of lithium-ion battery cells into a model predictive controller to optimally schedule charge/discharge, air-conditioning, and forced air convection power to shift a electric customer's consumption and hence reduce their electric bill. While linear state-of-health models produce linear relationships between battery usage and degradation, a non-linear, stress-factor model accounts for the compounding improvements in lifetime that can be achieved by reducing several stress factors at once. Applying this controller to a simulated system shows significant benefits from cooling-in-the-loop control and that relatively small sacrifices in bill reduction performance can yield large increases in battery life. This trade-off function is highly dependent on the battery's degradation mechanisms and what model is used to represent them.
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.
In late July 2018, the Energy Storage (ES) Safety Collaborative sent a survey to their stakeholders. The survey was designed to gather input and data to "support the timely deployment of safe energy storage technologies." The survey would also help to inform decisions related to enhancing ES efforts while "streamlining opportunities for collaboration amongst all relevant stakeholders." A total of 17 questions were included in the survey: 13 multiple choice questions and 4 open response questions. A total of 51 responses were collected and presented here are some of the high-level takeaways.
Batteries are designed to store electrical energy. The increasing variation in time value of energy has driven the use of batteries as controllable distributed energy resources (DER). This is enabled though low-cost power electronic inverters that are able to precisely control charge and discharge. This paper describes the software implementation of an open-source battery inverter fleet models in python. The Sandia BatterylnverterFleet class model can be used by scientists, researchers, and engineers to perform simulations of one or more fleets of similar battery-inverter systems connected to the grid. The program tracks the state- of-charge of the simulated batteries and ensures that they stay within their limits while responding to separately generated service requests to charge or discharge. This can be used to analyze control and coordination, placement and sizing, and many other problems associated with the integration of batteries on the power grid. The development of these models along with their python implementation was funded by the Grid Modernization Laboratory Consortium (GMLC) project 1.4.2. Definitions, Standards and Test Procedures for Grid Services from Devices.
Power systems can become unstable under transient periods such as short-circuit faults, leading to equipment damage and large scale blackouts. Power system stabilizers (PSS) can be designed to improve the stability of generators by quickly regulating the exciter field voltage to damp the swings of generator rotor angle and speed. The stability achieved through exciter field voltage control can be further improved with a relatively small, fast responding energy storage system (ESS) connected at the terminals of the generator that enables electrical power damping. PSS are designed and studied using a single-machine infinite-bus (SMIB) model. In this paper, we present a comprehensive optimal-control design for a flexible ac synchronous generator PSS using both exciter field voltage and ESS control including estimation of unmeasurable states. The controller is designed to minimize disturbances in rotor frequency and angle, and thereby improve stability. The design process is based on a linear quadratic regulator of the SMIB model with a test system linearized about different operating frequencies in the range 10 Hz to 60 Hz. The optimal performance of the PSS is demonstrated along with the resulting stability improvement.