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.
This paper discusses the development and current status of a recommended practice by the members of IEEE Working Group P2688 on Energy Storage Management Systems (ESMS) in grid applications. The intent of this recommended practice is to provide a reference for ESMS designers and ESS integrators regarding the challenges in ESMS development and deployment, and to provide recommendations and best practices to address these challenges. This recommended practice will assist in the selection between design options by supplying the pros and cons for a range of technical solutions.
Large-scale deployment of energy storage systems is a pivotal step toward achieving the clean energy goals of the future. An accurate and publicly accessible database on energy storage projects can help accelerate deployment by providing valuable information and characteristic data to different stakeholders. The U.S. Department of Energy's Global Energy Storage Database (GESDB) aims at providing high-quality and accurate data on energy storage projects around the globe. This paper first provides an overview of the GESDB, briefly describing its features and overall usage. This is followed by a detailed description of the procedure used to validate the database. In doing so, the paper aims at improving the usability of the website while enhancing its value to the community. Furthermore, the presented validation procedure makes the underlying assumptions transparent to the public so that data misinterpretation can be minimized/avoided.
Energy storage is an extremely flexible grid asset than can provide a wide range of services. Unfortunately, energy storage is often relatively expensive compared to other options. With the emphasis on decarbonization, energy storage is required to buffer the intermittency associated with variable renewable generation. This paper calculates the maximum potential revenue from an energy storage system engaged in day-ahead market arbitrage in the California Independent System Operator (CAISO) region and uses these results to estimate the distribution of break-even capital costs. Break-even cost data is extremely useful as it provides insight into expected market penetration given a target capital cost. This information is also valuable for setting policy related to energy storage incentives as well as for setting price targets for research and development initiatives. The potential annual revenue of a generic battery energy storage system (BESS) participating in the CAISO day-ahead energy market was analyzed for 2,145 nodes over a seven year period (2014-2020). This data was used to estimate the break-even capital cost for each node as well as the cost requirements for several internal rate of return scenarios. Based on the analysis, the capital costs of lithium-ion systems must be reduced by approximately 80% from current levels to enable arbitrage applications to have a reasonable rate of return.
This paper presents Energy Storage-based Packetized Delivery of Electricity (ES-PDE) that is radically different from the operation of today's grid. Under ES-PDE, loads are powered by energy storage systems (ESS) most of the time and only receive packets of electricity periodically to power themselves and charge their ESSs. Therefore, grid operators can schedule the delivery of electricity in a manner that utilizes existing grid infrastructure. Since customers are powered by the co-located ESSs, when grid outages occur, they can be self-powered for some time before the grid is fully restored.In this paper, two operating schemes for ES-PDE are proposed. A Mixed-Integer-Linear-Programming (MILP) optimization is developed to find the optimal packet delivery schedule for each operating scheme. A case study is conducted to demonstrate the operation of ES-PDE.
Substantial decreases in the cost of solar and energy storage systems create suitable conditions for implementing microgrids that operate independently from the main transmission/distribution grids. Such microgrids concept is particularly of interest for islanded and remote communities, which oftentimes rely on expensive energy resources to supply their demand. This paper presents the design of a microgrid for an island community, in which transmission infrastructure (an aging subsea cable that connects to the mainland grid) is replaced by solar and energy storage systems. Based on historical demand data and solar generation forecasts, an optimization framework is proposed to determine sizes of the microgrid components such that the local generation resources are self-sufficient and reliable. Results of this analysis show that, indeed, solar and energy storage systems are viable choices for implementing a microgrid and replacing transmission infrastructure.
This paper presents a literature review on current practices and trends on cyberphysical security of grid-connected battery energy storage systems (BESSs). Energy storage is critical to the operation of Smart Grids powered by intermittent renewable energy resources. To achieve this goal, utility-scale and consumer-scale BESS will have to be fully integrated into power systems operations, providing ancillary services and performing functions to improve grid reliability, balance power and demand, among others. This vision of the future power grid will only become a reality if BESS are able to operate in a coordinated way with other grid entities, thus requiring significant communication capabilities. The pervasive networking infrastructure necessary to fully leverage the potential of storage increases the attack surface for cyberthreats, and the unique characteristics of battery systems pose challenges for cyberphysical security. This paper discusses a number of such threats, their associated attack vectors, detection methods, protective measures, research gaps in the literature and future research trends.
This paper provides a study of the potential impacts of climate change on intermittent renewable energy resources, battery storage, and resource adequacy in Public Service Company of New Mexico's Integrated Resource Plan for 2020 - 2040. Climate change models and available data were first evaluated to determine uncertainty and potential changes in solar irradiance, temperature, and wind speed in NM in the coming decades. These changes were then implemented in solar and wind energy models to determine impacts on renewable energy resources in NM. Results for the extreme climate-change scenario show that the projected wind power may decrease by ~13% due to projected decreases in wind speed. Projected solar power may decrease by ~4% due to decreases in irradiance and increases in temperature in NM. Uncertainty in these climateinduced changes in wind and solar resources was accommodated in probabilistic models assuming uniform distributions in the annual reductions in solar and wind resources. Uncertainty in battery storage performance was also evaluated based on increased temperature, capacity fade, and degradation in roundtrip efficiency. The hourly energy balance was determined throughout the year given uncertainties in the renewable energy resources and energy storage. The loss of load expectation (LOLE) was evaluated for the 2040 No New Combustion portfolio and found to increase from 0 days/year to a median value of ~2 days/year due to potential reductions in renewable energy resources and battery storage performance and capacity. A rank-regression analyses revealed that battery round-trip efficiency was the most significant parameter that impacted LOLE, followed by solar resource, wind resource, and battery fade. An increase in battery storage capacity to ~30,000 MWh from a baseline value of ~14,000 MWh was required to reduce the median value of LOLE to ~0.2 days/year with consideration of potential climate impacts and battery degradation.
Variable energy resources (VERs) like wind and solar are the future of electricity generation as we gradually phase out fossil fuel due to environmental concerns. Nations across the globe are also making significant strides in integrating VERs into their power grids as we strive toward a greener future. However, integration of VERs leads to several challenges due to their variable nature and low inertia characteristics. In this paper, we discuss the hurdles faced by the power grid due to high penetration of wind power generation and how energy storage system (ESSs) can be used at the grid-level to overcome these hurdles. We propose a new planning strategy using which ESSs can be sized appropriately to provide inertial support as well as aid in variability mitigation, thus minimizing load curtailment. A probabilistic framework is developed for this purpose, which takes into consideration the outage of generators and the replacement of conventional units with wind farms. Wind speed is modeled using an autoregressive moving average technique. The efficacy of the proposed methodology is demonstrated on the WSCC 9-bus test system.
In this work, a model predictive dispatch framework is proposed to utilize Energy Storage Systems (ESSs) for voltage regulation in distribution systems. The objective is to utilize ESS resources to assist with voltage regulation while reducing the utilization of legacy devices such as on-load tap changers (OLTCs), capacitor banks, etc. The proposed framework is part of a two-stage solution where a secondary layer computes the ESS dispatch every 5-min based on 1-hr generation and load forecasts while a primary layer would handle the real-time uncertainties. In this paper, the secondary layer to dispatch the ESS is formulated. Simulation results show that dispatching ESSs by providing active and reactive support can minimize the OLTC movement in distribution networks thus increasing the lifetime of legacy mechanical devices.
The lack of inertial response from non-synchronous, inverter-based generation in microgrids makes the power system vulnerable to a large rate of change of frequency (ROCOF) and frequency excursions. Energy storage systems (ESSs) can be utilized to provide fast-frequency support to prevent such large excursions in the system. However, fast-frequency support is a power-intensive application that has a significant impact on the ESS lifetime. In this paper, a framework that allows the ESS operator to provide fast-frequency support as a service is proposed. The framework maintains the desired quality-of-service (limiting the ROCOF and frequency) while taking into account the ESS lifetime and physical limits. The framework utilizes moving horizon estimation (MHE) to estimate the frequency deviation and ROCOF from noisy phase-locked loop (PLL) measurements. These estimates are employed by a model predictive control (MPC) algorithm that computes control actions by solving a finite-horizon, online optimization problem. Additionally, this approach avoids oscillatory behavior induced by delays that are common when using low-pass filters as with traditional derivative-based (virtual inertia) controllers. MATLAB/Simulink simulations on a test system from Cordova, Alaska, show the effectiveness of the MHE-MPC approach to reduce frequency deviations and ROCOF of a low-inertia microgrid.
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.
The displacement of rotational generation and the consequent reduction in system inertia is expected to have major stability and reliability impacts on modern power systems. Fast-frequency support strategies using energy storage systems (ESSs) can be deployed to maintain the inertial response of the system, but information regarding the inertial response of the system is critical for the effective implementation of such control strategies. In this paper, a moving horizon estimation (MHE)-based approach for online estimation of inertia constant of low inertia microgrids is presented. Based on the frequency measurements obtained in response to a non-intrusive excitation signal from an ESS, the inertia constant was estimated using local measurements from the ESS's phase-locked loop. The proposed MHE formulation was first tested in a linearized power system model, followed by tests in a modified microgrid benchmark from Cordova, Alaska. Even under moderate measurement noise, the technique was able to estimate the inertia constant of the system well within ±20% of the true value. Estimates provided by the proposed method could be utilized for applications such as fast-frequency support, adaptive protection schemes, and planning and procurement of spinning reserves.
The lack of inertial response from non-synchronous, inverter-based generation in microgrids makes the power system vulnerable to a large rate of change of frequency (ROCOF) and frequency excursions. Energy storage systems (ESSs) can be utilized to provide fast-frequency support to prevent such large excursions in the system. However, fast-frequency support is a power-intensive application that has a significant impact on the ESS lifetime. In this paper, a framework that allows the ESS operator to provide fast-frequency support as a service is proposed. The framework maintains the desired quality-of-service (limiting the ROCOF and frequency) while taking into account the ESS lifetime and physical limits. The framework utilizes moving horizon estimation (MHE) to estimate the frequency deviation and ROCOF from noisy phase-locked loop (PLL) measurements. These estimates are employed by a model predictive control (MPC) algorithm that computes control actions by solving a finite-horizon, online optimization problem. Additionally, this approach avoids oscillatory behavior induced by delays that are common when using low-pass filters as with traditional derivative-based (virtual inertia) controllers. MATLAB/Simulink simulations on a test system from Cordova, Alaska, show the effectiveness of the MHE-MPC approach to reduce frequency deviations and ROCOF of a low-inertia microgrid.
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.