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SMOOTHING AND SHIFTING PV – APPLYING ENERGY STORAGE TO ENHANCE THE BENEFITS OF RENEWABLE ENERGY.

Olga Lavrova University of New Mexico Albuquerque NM 87131

Andrea Mammoli University of New Mexico Albuquerque NM 87131

Steve Willard, P.E. Public Service Co. of NM MS 1010 Alvarado Square Albuquerque NM 87158

Brian Arellano Public Service Co. of NM MS 1010 Alvarado Square Albuquerque NM 87158

Jon Hawkins Public Service Co. of NM MS 1010 Alvarado Square Albuquerque NM 87158

Brian McKeon Ecoult 402 Grafton Bond Building, 201 Kent St, Sydney, Australia

ABSTRACT Renewables and specifically photovoltaics (PV) are playing an ever increasing role in the resource mix for utilities across the nation. These resources pose new integration challenges compared to traditional generation. Higher penetrations of PV can lead to voltage instability due to intermittencies related to cloud cover and the output of PV is generally non-coincident to utility system load peaks. One prospective solution to this intermittency is battery energy storage, designed and controlled to smooth and shift PV output. At a recently commissioned project by the Public Service Co. of New Mexico (PNM), advanced lead acid batteries are utilized to shape the variable output of a 500kW PV resource to more beneficially impact the utility grid This ARRA/DOE funded Smart Grid Storage Project is testing and optimizing both smoothing and shifting capabilities of the batteries and their ability to create a dispatchable, firm and reliable renewable resource. As part of this project, detailed computer models of

the local utility grid were first developed to further understand integration of the batteries and PV. The results of actual field operation, combined with the underlying models are presenting a clear path in terms of optimizing battery size, and control algorithms, as well as offering a benchmark for measuring the benefits of battery storage. These results, coupled with the optimization capabilities of the models will lend the industry key results that are translatable to a variety of PV installations. 1. INTRODUCTION Intermittent renewables present a challenge to utility system operations. Renewable Portfolio Standards mandate levels of renewable resources, and associated variations in power production that weren’t contemplated in traditional system designs. Distribution systems are designed to deliver power from the transmission system to utility customers. The design intent of distribution systems is to keep voltage and frequency levels within standard limits at all times for the utility customers. However, these

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systems have traditionally been designed for one-way power flow and are now being affected by large placements of Solar PV. This renewable resource is appearing on the distribution grid, more so than the transmission grid, due to modularity, associated costs of integrating large systems as well as tax incentives favoring customer and smaller utility owned sites. The intermittency of PV, its effects on the distribution system and solutions to these effects are presented in this paper. PV is a pure current source and when insolation stops power production stops simultaneously. One way to characterize the difference is to analyze ramp rates.

Fig. 1: Measured PV Ramp Rates

As can be seen in Fig. 1 the change in output for PV can be quite drastic – in this case the output of the referenced PV resource changed 26% in one second. Additionally the ramp rates can be altered by numerous factors related to a given resource including cloud type, panel type, and system size; larger systems benefit from spatial diversity where a single cloud has less effect on output. This intermittency is a concern to utilities that are incorporating more and more PV. As higher levels of PV penetration appear, the utilities must respond in order to keep served voltage at customer premises within prescribed limits. Voltage stability has traditionally been dealt with through operation of devices like voltage regulators, load tap changers (LTC) on transformers and capacitor banks. Studies (EPRI, 2011) have shown that above certain penetrations of PV the ability of

these traditional means will be limited and that excessive wear on devices like LTCs will cause premature failures. Further, the location of larger PV resources will present different stability issues and different solutions; PV at the end of a feeder will present different challenges compared to PV placed next to a substation. Another factor that utilities have to deal with in terms of renewables is misalignment of renewable peak production to system load peaks. . While there is better alignment of PV to utility summer peaks (compared to wind), never-the less, these PV peaks occur typically 2 hours or more prior to system peaks in the summer due to thermal lag in the consumer facilities, as shown in Fig. 2. There is even less alignment of PV peaks to winter and shoulder peak loads.

Fig. 2: Alignment of Solar Peak to System Peak

2. SOLUTION In order to position for current and future growth of PV, PNM has approached the challenges of renewables through a foundational effort aimed at defining the ability of energy storage systems to address intermittency needs and make renewable resources even more valuable. This effort started with internal road-mapping and then lead to a Smart Grid Demonstration project with the Electric Power Research Institute (EPRI) that produced thorough studies on how distributed resources like PV and associated storage systems could be integrated into the gird. Furthering this effort Requirements Definitions were generated to firmly define the communication and control systems required for

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integration of these new resources in a cyber secure environment. PNM then took the next step and through ARRA funding manifested the Prosperity Energy Storage Project under the DOE’s Smart Grid Storage Program. This project entailed development of models of PV and storage, in conjunction with the University of New Mexico (UNM), and ultimately construction of a 500kW PV resource with an associated utility scale battery developed by East Penn Manufacturing and their subsidiary Ecoult. The system also employs a high resolution data control and acquisition system with Northern New Mexico College providing data analysis. Sandia National Labs (SNL) also provides assistance with algorithm development and data analysis. The project has numerous goals which target achievement of numerous benefits including 15% reduction of peak capacity on the feeder, creation of a firm, dispatchable renewable resource as well as industry translatable models of high penetration PV feeders with storage. The modeling has been conducted in two platforms, OpenDSS from EPRI and GridLAB-D TM. The main feature of the project is to demonstrate simultaneous PV smoothing and shifting. 2.1 Smoothing Algorithm The first area of modeling focused on the smoothing control algorithm and work here will continue throughout the next few years. The first algorithm used in modeling and in the field was developed at Sandia National Labs and used a simple moving average technique to instruct the battery control system on how to respond to changes in PV output. The algorithm in its basic form is as follows (Hund, Gonzales, & Barrett, 2009)

Inverter Output Watts =( Ir x Ki)+( Cl - C50%) x Ks Ir = Running Average Irradiance in W/m2 Ki = Constant (Irradiance Scale Factor) Cl = Measured Battery Charge Level in Wh C50% = Battery Charge at 50% in Wh Ks = Constant (Battery Charge Scale Factor)

The algorithm can be tuned by changing input parameter and gains within the equation. The performance of the battery system can be further tuned by changing the amount of smoothing capacity delivered at a given time. Alternative smoothing algorithms were also modeled in order to determine the optimal approach to smoothing. Three variations of the algorithm were modeled: moving average, double or dual moving average and the moving median, Development efforts have shown that the battery’s State of Charge (SoC) must be taken into account as a constraint when optimizing for the best smoothing performance. Results show that when SoC is considered the double moving average produces the best results. This is evidenced in Fig. 3 where the double moving average produces the smoothest results.

Fig. 3 Smoothing Modeling Output

2.2 Shifting Algorithm Initial efforts on the shifting algorithm have centered on a basic algorithm that predicts PV production for the next day and prioritizes how much energy is stored and when and how much energy is delivered. This strategy has the flexibility to change from summer to shoulder to winter operation when different peak times occur and in winter when dual daily peaks occur. Development of this algorithm

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began with the creation of a power prediction engine that uses next day percent cloud cover prediction. This engine was tuned through correlation analysis of historic forecast and actual site irradiation and PV power production data. Results show that for clear day prediction the engine correlates well with the predicted curve matching production, Fig. 4. For cloudy days the correlation was not strong as shown in the R squared analysis with the predicted % cloud cover consistently overestimating cloud cover, see Fig. 5. This lack of correlation points to the need for improved PV forecasting; a weak day –ahead forecast will diminish the economic benefit of storage.

Fig. 4 Alignment of Predicted and Actual Clear Day Power   

Fig. 5 Correlation of Cloudy Day Prediction to Actual Cloudy Day Power

The engine is now being utilized to generate, based on next day predictions, operating levels of shifting battery charge and discharge for successive time intervals throughout the day. The following graph below, Fig. 6, shows a predicted output curve of the PV and Battery combination and battery output. As work progresses over the next few years the

algorithm will be tailored to include price forecasts, overall system and distribution load forecasts. This will entail feeding day-ahead price and load signals from the wholesale marketing group as well as day -ahead PV forecasts. The shifting algorithm will also be tuned to peak shaving where distributions system based signals (as opposed to system based signals used for firming) will be used to target a 15% reduction of load on the feeder.

Fig. 6: Predicted Output of Smoothing and Shifting 3. FIELD RESULTS

The fielded system was commissioned in September 2011 and consists of a 500kW PV field consisting of standard C-Si panels and an inverter along with a complete battery system from East Penn/Ecoult. The battery system is composed of 8 shipping containers, 2 of which contain the Ultrabttery© dedicated to smoothing PV output and 6 of which contain Advanced Lead Acid batteries dedicated to shifting. A robust data acquisition system collects 1 second data for 208 points in the system, including metrology, PV string parameters, battery points and utility grade metering, including phasor measurement units. The data gathering effort is centered on five distinct plans testing individual functionality and then combining all functionality in the final test plan. The tests include:

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Smoothing

Firming (shifting to a system based signal)

Peak Shaving (shifting to a feeder/substation based signal)

Arbitrage

All the above combined

3.1 Smoothing Results An initial smoothing algorithm has been implemented utilizing a single moving average. The algorithm has been tested using various battery capacities and different input signals including, Site meter, PV meter, and the average of 5 irradiance sensors placed throughout the PV field. Data analysis is indicating that correlation of season and cloud type is necessary to optimize and determine the best algorithm configuration; different variations of inputs may be ultimately necessary for different seasons and cloud types.

Fig. 7 displays results with a PV meter input signal to the smoothing algorithm with the yellow trace indicating the battery, blue the raw PV output and red the smoothed PV output , Fig. 8 presents a magnified view of the same day. In terms of optimization the results will be gauged in feeder models with high penetration PV and the effect on Load Tap Changers (LTC) will be analyzed to determine the best signal input and minimal level of battery capacity needed.

Fig. 7: Actual Smoothing Data Using PV Meter Input 1/21/2012 Red=Primary Meter, Yellow=Battery, Blue= PV Output

Fig. 8: Magnified View of Smoothing Data PV Meter Input

Subsequent trials used the irradiance sensor average as an input to the smoothing algorithm. . Fig 9 displays results with the irradiance sensor average as an input and 40% battery capacity; Fig. 10 is a magnified view of the same day I. These data sets show less mitigation of the ramp rate. Further data analysis is under way to quantify specific effects of various test schemes and optimize the amount of beneficial smoothing battery capacity for a given PV resources.

Fig 9: Actual Smoothing Data Using Average Irradiance Sensor Input 2/19/2012

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Fig. 10: Magnified View of Smoothing Data Using Average Irradiance Sensor Input

3.2 Shifting Results

The shifting algorithm has been implemented in a basic form. As mentioned, this algorithm takes a percent cloud cover forward forecast from the National Weather Service and runs a predictive power analysis which is then used to predict the required shifting battery schedule. This analysis is embedded in an Advanced Calculation Engine (ACE) integrated with the data historian serving the project. Calculated charge and discharge values from the ACE system and an associated schedule are sent to the battery system controller. Initial results of shifting are shown in the following Fig: 11

Fig: 11: Initial Demonstration of Shifting Algorithm

These results show good alignment with the predictive model considering that the schedule is manually input in 15 minute increments and will be tuned to much finer granularity in an automated basis in the next evolution of the algorithm, expected to be in place in Q2 of 2012.

Alignment of the models for shifting was tested to ascertain the accuracy of system and battery model output as well as state of charge calculations by comparing these to actual measurements in the field during smoothing and shifting operation on February 21, 2012.

Fig. 12 compares the modeled system output to actual output. The model results are well tuned until deviation from the cloud cover occurs in the late afternoon. Similarly the system output for the modeled battery compares well to field data, shown in Fig. 13. Initial analysis indicates the need for a more accurate PV forecast than the currently day ahead percent cloud cover forecast. This forecast is generated by the National Weather Service and is more oriented to aviation purposes rather than solar PV production.

Fig. 12 Comparison of Predicted vs Actual Results of Shifting

Fig. 13 Comparison of Predicted to Actual Battery Output

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Fig. 14 Comparison of Predicted to Actual Battery State of Charge

In addition to the system and battery outputs the SoC predication was also tested. Fig. 14 shows close alignment between predicted and actual SoC. This is a good indication that the operating parameters of the battery are being correctly modeled.

Simultaneous shifting and smoothing was demonstrated on this same date. This involved manual implementation of the shifting algorithm while allowing the smoothing algorithm to run simultaneously. A magnified view of Fig: 11 is presented in Fig. 15 where the simultaneous shifting and smoothing occur.

Fig. 15 Preliminary Demonstration of Simultaneous Smoothing and Shifting

As can be seen in Fig. 15,as the PV declined in the evening, cloud cover introduced intermittency while at the same time the shifting batteries started discharging to meet an evening peak. At this point

both shifting and smoothing batteries were working simultaneously.

4. CONCLUSION

The Prosperity Energy Storage Project is demonstrating multiple streams of benefits by simultaneously smoothing and shifting PV production. A strucutured test plan will allow these benefits to be maximized by tuning the algorithm and optimizing system capacity and performance. Over the next two years a variety of inputs will be tested and the algorithms will be developed with higher levels of complexity and multi-variant inputs. As more data becomes available in the field, the models will be further calibrated and used to intrinsically define and quantify benefits for a broader set of feeders.

The combination of field data and models will be further used to translate the benefits, not only to other feeders at PNM, but through the industry as a whole. Additionally the flexible design approach will allow the communication/control system to easily be scaled to both small, community based storage systems and large wind based storage systems in a cyber secure environment.

5. ACKNOWLEDGMENTS

The Authors also wish to recognize the invaluable assistance of Abraham Ellis of Sandia National Labs.

This material is based upon work supported by the Department of Energy under Award Number DE-OE0000230

Disclaimer

This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States 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

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name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

6. REFERENCES

EPRI. (2011). Impact of High-Penetration PV on Distribution System Performance TR 1021982. Palo Alto CA: Electric Power Research Institute.

Hund, T., Gonzales, S., & Barrett, K. (2009). Grid-Tied PV System Energy Smoothing. IEEE PVSC10A9_3 .