Using a two-way inverter to convert between AC and DC power, batteries can store power when the electricity from the grid is available and discharge to meet the load during a time of power outage. Advances in electric vehicle energy storage technology have led to a sustained decrease in the price of electrical storage. We studied the use of battery storage to allow a set of homes in a single residential neighbourhood to avoid power outages. In essence, the entire neighbourhood can be thought to be connected to a single large uninterruptible power supply.
Storage is still expensive, however, so our goal is to choose the smallest battery size such that, with high target probability, there is no loss of load despite a grid outage. Recognizing that the most common approach today for mitigating outages is to use diesel generators, we also study the related problem of minimizing the carbon footprint of diesel generator operation by minimizing generator operation.
Access the paper here
S. Singla. On Using Storage and Genset for Mitigating Power Grid Failures, MMath thesis, University of Waterloo, April 2013.
Detecting anomalies in solar power generation
Solar panels lose efficiency when they’re covered with dust, dirt or snow, or are shaded. Although cleaning them is not a huge issue, keeping an eye on them to determine when they need cleaning is physically demanding, because solar panels are usually installed in inaccessible locations like rooftops. So as part of our study, we explored some automated methods to detect soiling.
In our first study, we used solar power traces to detect soiling using an algorithm we developed. We were able to detect ten types of anomalies, including temporary shading, permanent shading, fallen leaves, accumulating snow, and melting snow .
While working with solar panels, we noticed that shading affects power output in a different way than soiling. In our second study, we used solar power traces along with solar intensity information to detect obstructions of both types. Our detection algorithm achieved an 85% accuracy when tested on two real PV installations in Ontario, Canada .
 B. Hu. Solar Panel Anomaly detection and classification, MMath thesis, University of Waterloo, May 2012.
 X. Gao, L. Golab, S. Keshav, What’s wrong with my solar panels: a data-driven approach, Proc. Workshop on Energy Data Management, March 2015, pp. 86-93.
Allowing solar farms to enter into firm contracts with electric grid utilities (TSOs)
A large barrier to integrating solar power into the electric grid is the variability of sunlight. Because solar power generation depends on the intensity of sunlight in the moment, it’s tough to control solar energy generators to match fluctuations in demand from the electric grid. This means solar farms cannot confidently participate in the day-ahead electricity market, unless they add generous safety margins when committing to a certain level of power generation for the next day.
To allow solar farms to participate more confidently in the day-ahead electricity market, we developed a technique that considers the variability of solar intensity to predict how much electricity can be generated in the upcoming day. We also show how the same technique can be used to determine the best storage size for a solar farm to help meet fluctuating demands in energy. We tested our technique using a 10-year dataset, and found that it attains 93% of the maximum revenue that would have been achieved in the daily market if the entire schedule of the sun’s intensity was known ahead of time.
For energy storage specifically, our model determines the optimal size by taking into account the following three factors that affect solar power generation:
- The position of the sun in the sky
- Long-term cloudiness at time scales ranging from 10-minutes to a few hours
- Short-term cloudiness that last less than 10-minutes
Given a target output power and an allowable loss of power threshold, our technique computes a near-optimal storage size.
 Y. Ghiassi-Farrokhfal, S. Keshav, and C. Rosenberg. Firming Solar Power, Extended Abstract/Poster, Proc. ACM SIGMETRICS, June 2013.
 Y. Ghiassi-Farrokhfal, S. Keshav, C. Rosenberg, and F. Ciucu. Solar Power Shaping: An Analytical Approach, IEEE Transactions on Sustainable Energy, Vol 6, No. 1, Jan. 2015.
Optimal budget allocation
Suppose you had a million dollars to spend, some on solar panels and some on a storage system. While adding more solar panels increases the amount of power your solar farm can generate, adding more storage means you can source excess power on days with less sun so that the overall power supply is less varied. With this in mind, what would be the best way to split your budget if you’d like to maximize revenue by selling electricity in the day-ahead market? We provide an algorithm to determine this, taking into account factors such as:
- the power level committed by a solar farm owner
- the capacity of the transmission link between the solar farm and the power grid
- the need to curtail excess solar generation
- the degree in fluctuation in the purchase price of energy
- the inherent variation in solar generation due to fluctuations in sunlight
Ghiassi-Farrokhfal, F. Kazhamiaka, C. Rosenberg, and S. Keshav, Optimal Design of Solar PV Farms with Storage, IEEE Transactions on Sustainable Energy, October 2015.
As grid-provided electricity continues to rise in cost, individuals, small companies, and building operators are increasingly looking to offset grid usage using solar photovoltaic (PV) panels and energy storage. When constrained by a budget however, it is difficult to predict the optimal ratio of solar PV panels and storage that one should buy to completely or partially offset grid usage.
We tested three ways to determine the possible sizing(*) required to meet the anticipated load in an off-grid system. Out of the three methods–simulation, optimization, and stochastic network calculus–we found that the easiest method, simulation, also provided the best results.
(*) sizing: the power or energy size of the storage in kW/kWh, and the size of solar generation in kWp.
We are currently building a web application to help predict the best sizing for a solar energy system. The tool will be linked here when completed.
F. Kazhamiaka, S. Keshav, and C. Rosenberg, Robust and Practical Approaches for Solar PV and Storage Sizing, Proc. ACM eEnergy 2018, June 2018.
The best-known lithium-ion battery model whose parameters can be calibrated entirely from a battery’s manufacturer-provided specifications (ie. internal resistance, nominal capacity, voltage at full charge, etc) was proposed by Tremblay et al and is widely use. However, it has some shortcomings. For instance, it has low fidelity at high charge and discharge rates. Moreover, it only models a cell, and does not model the battery management system. This makes it unsuitable for evaluating practical storage systems.
We propose an alternative, the Power-based Integrated (PI) model, whose parameters can also be calibrated entirely from manufacturer specifications, but has much higher fidelity across a wide range of charge/discharge rates. This model is freely available in the public domain as a Matlab system block compatible with Simulink simulation software. Access the block here.
We suggest using our model when:
- Simulating an energy system with storage by modeling its power flows. In this case, it is desirable to have a battery model that uses power as input because power is conserved (note that the Tremblay model uses current as input). The PI model uses power as input.
- Modeling the battery management system (BMS) – the BMS protects the cells in a battery from being damaged due to improper use such as under/over-charging. Modelling the BMS in the PI model prevents the simulated battery from being used in unrealistic ways.
- Low error – based on validation performed in the experiment, we see that the PI model has a mean absolute voltage error of less than 0.1V across a wide range of C-rates.
F. Kazhamiaka, S. Keshav, C. Rosenberg, and K.-H. Pettinger, ”Simple Spec-Based Modelling of Lithium-Ion Batteries, ” IEEE Transactions on Energy Conversion, Vol 33, No. 4, December 2018.