Integrating renewable resources such as solar photovoltaic (PV) onto the grid can create challenges for system operators. Fortunately, energy storage technologies have been identified as a potential resource to help operators deal with their intermittent nature.

The Public Service of New Mexico (PNM) is addressing this issue as part of a smart grid demonstration project it is conducting with the Electric Power Research Institute (EPRI) and the U.S. Department of Energy’s (DOE’s) smart grid energy storage program with two related projects. First, PNM is demonstrating a system that uses energy stored in batteries to mitigate voltage fluctuations through battery smoothing and manage peak demand through battery shifting simultaneously. Second, PNM is collaborating on a demonstration of a commercial building micro-grid designed and built by the Japanese government’s New Energy and Industrial Technology Development Organization (NEDO).

Energy from solar PV systems can introduce challenges on the distribution system when a high level is integrated into the grid. Smoothing PV output is important because the ramp rates can be very fast. Ramp rates on PNM’s 500-kW PV installation have been recorded as fast as 135 kW/sec. This can cause unacceptable voltage variation on an associated feeder.

Also, because peak production from the PV system does not align with the times when energy is needed most on the system, energy storage can be used to shift PV energy to meet the system peak. This includes firming, which is the ability to guarantee constant power output to the electricity market during a certain period of time, and peak shaving, which is the ability to limit the load on a given feeder.

Major System Components

PNM draft graphic

Combined with the 500-kW PV installation, PNM’s energy storage system is comprised of Ecoult/East Penn Manufacturing Synergy advanced lead-acid batteries with an energy rating of 1 MWh for shifting and UltraBattery advanced lead-acid batteries with embedded ultracapacitors and a power rating of 500 kW for smoothing. The UltraBattery is built for quick response, operating at a high discharge and charge rate.

A data acquisition system had to be developed to record data and provide control signals for the project. The requirements included the capability of receiving and transmitting data from multiple sensors and control systems on site with sample rates of at least 1 second. Working with one of the project partners, Sandia National Laboratories, PNM installed phasor measurement units (PMUs) to obtain a very high sample rate of 30 samples/sec — granular data for characterizing the two components.

Cybersecurity also is addressed in a defense-in-depth approach, providing firewall, authentication, access management and auditing capabilities for access attempts to the site. Interoperability was facilitated by up-front use-case analysis using EPRI’s IntelliGrid methodology and adhering to standards identified through the Smart Grid Interoperability Panel (SGIP).

The commercial building micro-grid combines multiple customer-side generation technologies, storage technologies and an advanced energy management system (EMS) to create a self-optimizing building that can island for short periods of time. The generating resources include 50 kW of PV, a 240-kW natural gas generator and an 80-kW phosphoric-acid fuel cell. Energy storage resources include 150-kWh advanced lead-acid batteries, and thermal storage from heat recovery from the fuel cell and natural gas generator using water tanks. The water can be used to heat the building or used in conjunction with an absorption chiller to provide cooling. The building micro-EMS provides real-time control and optimization of building resources and provides schedule optimization.

Basic Methodology

The project used a high degree of modeling to support both predictive feeder response as well as smoothing and shifting algorithm development. The modeling effort used feeder data, predictions of PV energy output, battery response and response of the micro-grid in both EPRI’s Open Distribution System Simulator (OpenDSS) as well as Pacific Northwest National Laboratory’s GridLAB-D software. The battery-response modeling allowed development and optimization of both smoothing and shifting algorithms.

Cloudy PV production

The smoothing algorithm, developed by Sandia National Laboratories, resides within the battery controller. This power-based functionality requires fast reaction times to smooth intermittent PV output effectively, with the PV kilowatt output acting as a control signal. Because batteries are still a high-cost material, optimizing the degree of smoothing needed versus battery energy use is a critical endeavor. The algorithm has two modes of operation, a low-pass filter and a moving average. The gains, time constants and moving average window all are adjustable from the back-office software to ensure optimization. Both the moving average and low-pass filter were evaluated for effectiveness as a function of optimized battery use. Additional functionality was embedded into the smoothing algorithm by providing two auxiliary inputs to the logic. These auxiliary inputs were used for the coordinated control of the NEDO micro-grid.

The shifting control was designed using an algorithm residing in the PNM back office. The shifting algorithm is hosted in the back office because of the data types needed and the desire not to transmit extensive and sensitive data to field devices, conserving communication bandwidth and data security. Because of the lack of a commercially available grid-level battery control system, the shifting control strategy was based on calculating a setpoint for the system using an advanced calculation engine that sends the command to the battery control system. The algorithm polls real-time power market prices, supervisory control and data acquisition (SCADA) data and weather predictions from the National Oceanic and Atmospheric Administration (NOAA).

Based on these and other parameters, the shifting algorithm optimizes the delivery of stored energy on a day-to-day basis. To shave peak, the algorithm predicts the next day’s peak feeder load by correlating weather data, historic feeder loads, PV-output prediction, and correlations of different intermittences and types of cloud cover.

The building micro-grid operated by NEDO uses multiple variables to optimize building performance through the micro-EMS. Specific tests of coordinated control between the micro-grid and PNM’s battery storage system have recently been performed to study how the two systems can collaborate to benefit the local distribution system.

Utilities will have an increasing need to understand and collaborate with customer-owned distributed resources for overall system balancing. In this demonstration, data from the PV and battery system are actively passed every second to the building micro-grid, while the building micro-grid passes 1-sec data from the NEDO gas engine and fuel cell to the PNM battery control system. PV intermittency data is used as an input to the natural gas generator and fuel cell to allow the two resources to provide ramping support. Simultaneously, data is passed from the natural gas generator and fuel cell to the battery control system. The smoothing algorithm uses this data to reduce the duty on the battery system.

Demonstration Results

Peak shaving

The developed algorithms have successfully addressed both smoothing and simultaneous energy storage for dispatch at a time more valuable to the utility. Through simultaneous operation, the algorithms are able to take extremely variable PV output that typically would be difficult to regulate and create a defined output in both amplitude and duration during the system peak. The storage system is able to take available energy produced by PV, store it in the battery system with very little production to the grid and dispatch it as a predictable output, providing a firm block of energy aligning with the evening peak load.

The peak-shaving algorithm has been optimized and refined, and has enabled PNM to meet a target 15% reduction in peak summer load on the associated feeder. The shifting control algorithm understands the need for a peak load reduction on the feeder as opposed to a firming control. The algorithm dispatches the battery system to successfully reduce the peak that the distribution system would otherwise have to manage.

Coordination between the building micro-grid and battery storage system was demonstrated. Multiple models and simulations were conducted using archived data from the individual systems. The models studied optimization of the resources and created setpoints for the smoothing algorithm variables such as gains and time constants. Data from the NEDO micro-grid and energy storage site were exchanged on a cloudy day in a closed-loop control system. Results of the interaction are currently under analysis.

Next, during a clear day, PV intermittency was simulated by opening a breaker to disconnect a subset of PV strings. This controlled disconnection provided a known drop-in PV output that could be used more precisely for calculating response by the battery system as opposed to a variation based on the degree and opacity of cloud cover.

Lessons Learned

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