We are currently observing a multi-disciplinary research thrust to re-design the electricity grid. The overarching objective of this thrust is to have an efficient distribution system that is also robust. My research on this topic is primarily involved with the design of the communication among the entities of the grid — users, operators, suppliers — that will be supported by the adoption of smart meters and with the design for seamless integration of growing renewable energy generation into the grid.
In this project we consider the problem of matching power production to power consumption. This problem is exacerbated by the introduction of renewable sources, which, by their very nature, exhibit significant output fluctuations. This problem can be mitigated with the introduction of a system of smart meters.Smart meters control the power consumption of customers by managing the energy cycles of various devices while also enabling information exchanges between customers and the system operator as well as between customers themselves. The information flow and control abilities can be
combined with sophisticated pricing strategies so as to encourage a better match between power production and consumption. The effort of power providers to regulate the consumption of end users is referred to as demand response management. In our work, we study the rational consumer behavior in a repeated noncooperative game with incomplete information when the power provider employs an adaptive pricing policy. The adaptive price depends on renewable source output and total power consumption, and hence incentivizes customers with heterogeneous preferences to anticipate behavior of others and beware of their influence on price. Given the adaptive pricing strategy, we formulate the power consumption behavior of customers as a repeated noncooperative game with incomplete information. We provide an explicit characterization of unique Bayesian Nash equilibrium strategy when consumers only know their self-preference and the population preference distribution. Comparing the behavior of selfish consumers with the welfare maximizing consumers, selfish consumers tend to put more weight on their self-preference than on the mean population preference. The rational behavior is also characterized in a communication scheme where smart meters exchange consumption levels with neighboring meters. We use the QNG algorithm to compute equilibrium consumption and propagate beliefs. We observe that communication leads consumers to act similar to welfare maximizing individuals. In addition, communication is beneficial for welfare while having negligible effect on price and consumption levels. We further propose an ad-hoc pricing scheme that the operator can use to lower the peak-to-average ratio of total consumption by adjusting its target profit ratio [Eksin et al 2015, Eksin et al 2018].
At A&M this project evolved to consider intraday trading in electricity markets among system operators to increase reliability. In one work, we proposed an iterative mechanism for multiple areas to determine their terms of trading energy accounting for flow constraints. The iterative mechanism guaranteed convergence to a Nash equilibrium, a set of trading actions in which no area has unilateral profitable deviation. In such a solution some of the areas could lack the incentive to participate in the proposed mechanism as they may be worse off when they participate in the trading. Thus, we proposed a scheme for paying or charging participation fees, that creates an incentive compatible mechanism that is also budget balanced under certain technical restrictions [Garcia et al 2021]. A follow up study considers a similar iterative mechanism for improving intraday flexibility and reliability of areas [Khazaei et al 2022].
Collaborators and References
The project spun during my time at Boğaziçi University visiting Hakan Deliç’s lab. I thank Hakan Deliç for being a valuable and supportive collaborator in this project. At A&M, this project evolved in collaboration with Dr. Garcia, and grew with the support and hard work of our postdocs Roohallah Khatami and Hossein Khazaei, and PhD student Furkan Sezer.
At A&M, this project is partially supported by NSF Grants ECCS 1953694. CCF 2008855.