A tremendous amount of work still needs to be done to optimize and improve the effectiveness of energy storage. Optimizing the shifting algorithm has proved challenging because of the need for a power prediction that uses a next-day percentage of cloud cover. Despite successfully demonstrating the capability of weather predictions in the project, day-ahead weather forecasts are not sufficiently dependable or accurate. Additionally, enhancement is needed for back-office systems controlling distributed resources such as battery storage systems. This project had to rely on a system developed in-house to provide control and optimization of the storage site using software systems not specifically designed for this application.

As the number of energy storage systems increases, a mature control system strategy that can take into account the wide range of information needed to optimize the control and dispatch of multiple energy storage systems will be extremely important. Standardization of interfaces also will be an important step to create an interoperable and secure control system both for on-site and back-office control systems.

The coordination with a non-utility-owned micro-grid proved to be challenging. There was a great amount of learning about interconnecting and coordinated control. Interconnection requirements were evolving through efforts such as the IEEE 1547.4 standard during design and construction of the building micro-grid. Therefore, questions around how the system should disconnect during outages and where that disconnection capability resides were evaluated.

The ultimate desire to protect utility crews working on lines as well as the public, and to prevent further damage from feeding a fault, had to take precedence over the desire for the micro-grid to operate in an islanded mode. The final design needed to meet the functionality of safe disconnection as well as other standards such as surge-withstand capability and breaker rating for use in interrupting a rotating machine.

Coordinated control introduced challenges to algorithm development. The smoothing algorithm was designed to operate with PV systems. Introduction of systems such as natural gas generators will require taking into account both positive and negative ramps available in a generator. Changes in limits imposed on variables had to be re-evaluated. Latency of systems inherent in geographically separated systems will need further evaluation to determine the approach’s effectiveness.

PV Applications Advance

The PNM projects done as part of the EPRI smart grid demonstration and DOE smart grid energy storage program are providing a wealth of knowledge. Although rigorous in terms of planning through modeling, simulation and use-case development, the projects have shown there is no substitute for deploying actual equipment to provide information needed to advance a technology from demonstration to wide-scale deployment. Work on the projects will continue into early 2014 to complete the rigorous test plans associated with the projects. Additional efforts around optimization of the shifting algorithm, improvement in the cloud-cover prediction and further work on the micro-grid coordination are underway. A cost-benefit analysis of the energy storage system based on the results also is underway.

Acknowledgment

Steve Willard, PNM project manager and principal investigator for the DOE energy storage project, and Brian Arellano, PNM project manager for advanced technology, contributed to this article. This material is based on work supported by the DOE under award number DE-OE0000230. The authors also recognize the contributions of the DOE, NEDO, Sandia National Laboratories and the University of New Mexico.


Jonathan Hawkins (jon.hawkins@pnmresources.com) is the manager of advanced technology and strategy at PNM Resources. His team is responsible for the evaluation and proposal of applications of emerging technologies in support of PNM Resources’ strategic objectives. Areas of responsibility include smart grid technologies and strategy, integration of distributed energy resources, plug-in hybrid electric vehicles and storage technologies. Hawkins earned his BSEE degree from the University of New Mexico.

John J. Simmins (jsimmins@epri.com) is a technical executive at the Electric Power Research Institute, where he manages the information and communications technology for distribution project set. His current responsibilities focus on bringing thought leadership in the area of integrating diverse applications such as advanced metering infrastructure, meter data management systems, distribution management systems, customer information systems, geospatial information systems and outage management systems. Simmins also leads the EPRI efforts in the use of augmented reality, social media, data analytics and visualization to improve outage-restoration efforts and grid resilience. He received his bachelor’s degree and a Ph.D. in ceramic science from Alfred University in 1984 and 1990, respectively.

Karen George (kgeorge@epri.com) is a project manager at the Electric Power Research Institute on the smart grid demonstration initiative, focusing on technology transfer. She has served as an analyst, research director and technical writer in multiple areas for EPRI, focusing on smart grid, efficiency, demand response and customer behavior research. Prior to joining EPRI, she served as a consultant and manager in areas related to renewable energy and residential building efficiency for several organizations, including the Colorado Energy Office and the University of Colorado at Boulder School of Civil, Environmental and Architectural Engineering.

Required Disclaimer. This work was prepared as a partial account of work sponsored by an agency of the U.S. government. Neither the U.S. government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the U.S. government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the U.S. government or any agency thereof.

Companies mentioned:

Department of Energy | http://energy.gov

Electric Power Research Institute | www.epri.com

GridLAB-D | www.gridlabd.org

Ecoult | www.ecoult.com

IEEE | www.ieeeusa.org

New Energy and Industrial Technology Development Organization | www.nedo.go.jp/english

Pacific Northwest National Laboratory | www.pnl.gov

Public Service of New Mexico | www.pnm.com

Sandia National Laboratories | www.sandia.gov

SGIP | http://sgip.org

University of New Mexico | www.unm.edu