• Renewable Energy Integration

    PV-Storage system profitability in different jurisdictions

    Policy makers in many jurisdictions have implemented incentive schemes such as `feed-in tariffs’ (FIT) and upfront purchase subsidies to encourage consumers to self-generate parts of their power requirements by solar energy. We quantitatively study the impact of jurisdiction-specific solar radiation profile, the typical residential loads, the cost of system components, the price of grid electricity, and incentive programs on photovoltaic (PV) and storage system profitability in Germany, Ontario, and Austin, Texas.
    In each jurisdiction, for a range of PV and storage system sizes,
    we compute the optimal use of the system,  and hence the best possible profitability of that system in that jurisdiction over a 20 year life span. This methodology allows us to quantitatively estimate the influence of a jurisdiction on the (best possible) profitability of PV-storage systems. We find that the choice of jurisdiction has significant impact on the profitability of PV-storage systems. We also find that policy makers can use the price of grid electricity as well as upfront subsidies to influence profitability, and therefore adoption.

    Using ABMs to understand PV and storage adoption

    The adoption of solar photovoltaic panels and batteries greatly reduces a grid customer’s carbon footprint, while simultaneously reducing their dependency on conventional electricity supply. Given
    the significance of both outcomes, it is important to understand the
    potential effect of energy policies on the adoption of these ‘PV-
    battery systems’ before they are actually implemented. We therefore design and implement an Agent-Based Model (ABM) that captures the purchase and usage of PV-battery systems. Focusing on Ontario, we use a survey to elicit the responsiveness of residents to potential energy policies. We parameterize the ABM based on survey results to forecast the relative performance of different energy policies. We find that PV-battery system adoption in Ontario is likely to be incremental rather than exponential. Moreover, we find that, of all the policies we evaluated, the most effective way to improve PV-battery system adoption is to significantly reduce its price.

    Solar power shaping

    The increasing penetration of variable-energy sustainable generators (e.g., solar and wind) has raised the question of how best to manage them as replacements for the coal and fuel energy sources. One of the significant barriers for complete replacement is the burstiness of the short-term and long-term variations of sustainable generators. In particular, for solar energy, the long-term variation due to the variations induced by the position of the sun in the sky  quite predictable but short-term variation is due to clouds may attenuate or even enhance (counter-intuitively)  ground level solar irradiation. This short-term variation can be modeled as a stochastic process that can be studied using techniques from stochastic network calculus.

    Day-ahead revenue maximization

    Most large-scale generators of electrical power enter into daily or hourly contracts with market makers such as Regional Transmission Organizations, where they pre-commit to a certain constant level of power generation. The payment is proportional to the power level and suppliers must pay a penalty if the commitment is not met. The high variability of solar power, due to intrinsic diurnal variability as well as additional stochastic variations due to cloud cover, have made it difficult for solar farms to enter these markets. We therefore study the use of battery storage to ‘firm’ solar power, that is, to remove variability so that such pre-commitments can be made. Due to the high cost of storage, it is necessary to size the battery parsimoniously, choosing the minimum size to meet a certain reliability guarantee.


    • Y. Ghiassi-Farrokhfal, S. Keshav, and C. Rosenberg. Firming Solar Power, Extended Abstract/Poster, Proc. ACM SIGMETRICS, June 2013.


    Budget splitting between solar panels and storage

    We consider the problem of allocating a capital budget to solar panels and storage to maximize the expected revenue in the context of a large-scale solar farm participating in an energy market. This problem is complex due to many factors. To begin with, solar energy production is stochastic, with a high peak-to-average ratio, thus the access link is typically provisioned at less than peak capacity, leading to the potential waste of energy due to curtailment. The use of storage prevents power curtailment, but the allocation of capital to storage reduces the amount of energy produced. Moreover, energy storage devices are imperfect. A solar farm owner is thus faced with two problems: 1)deciding the level of power commitment and 2) the operation of storage to meet this commitment. We formulate two problems corresponding to two different power commitment approaches, an optimal one and a practical one, and show that the two problems are convex, allowing efficient solutions. Numerical examples show that our practical power commitment approach is close to optimal and also provide several other engineering insights.