• Energy Research

The most severe failures in power grids are often characterized as cascading failures where an initial event triggers consequent failures all along the grid often leading to blackouts. Upon identifying a failure and its cascade potential, timely control actions should be performed by the grid operators to mitigate the effect of the cascade. These actions have to be delivered to one or more control devices, creating a dependency between the power grid and its control network. This paper examines the dependency of the operation of the power grid on the control network. Different from literature studies on failure control, our dependency model captures the impact of networking parameters. We formulate an algorithmic model that describes the impact of this dependency on cascade control. Based on this model, we propose an efficient cascade control algorithm using load shedding with consideration of delays in the communication network for power grids. Finally, we evaluate the impact of the power-communication network dependency with uncontrolled grids, ideal/simple control grids and our proposed control scheme. The results demonstrate that the proposed algorithm can significantly reduce the failure of power lines while sustaining larger power demand for users.

Identifying a potentially large number of simultaneous line outages in power transmission networks in real time is a computationally hard problem. This is because the number of hypotheses grows exponentially with the network size. A new "Learning-to-Infer" method is developed for efficient inference of every line status in the network. Optimizing the line outage detector is transformed to and solved as a discriminative learning problem based on Monte Carlo samples generated with power flow simulations. A major advantage of the developed Learning-to-Infer method is that the labeled data used for training can be generated in an arbitrarily large amount rapidly and at very little cost. As a result, the power of offline training is fully exploited to learn very complex classifiers for effective real-time multi-line outage identification. The proposed methods are evaluated in the IEEE 30, 118 and 300 bus systems. Excellent performance in identifying multi-line outages in real time is achieved with a reasonably small amount of data.

Mechanism design is studied for aggregating renewable power producers (RPPs) in a two-settlement power market. Employing an indirect mechanism design framework, a payoff allocation mechanism (PAM) is derived from the competitive equilibrium (CE) of a specially formulated market with transferrable payoff. Given the designed mechanism, the strategic behaviors of the participating RPPs entail a non-cooperative game: It is proven that a unique pure Nash equilibrium (NE) exists among the RPPs, for which a closed-form expression is found. Moreover, it is proven that the designed mechanism achieves a number of key desirable properties at the NE: these include efficiency (i.e., an ideal "Price of Anarchy" of one), stability (i.e., "in the core" from a coalitional game theoretic perspective), and no collusion. In addition, it is shown that a set of desirable "ex-post" properties are also achieved by the designed mechanism. Extensive simulations are conducted and corroborate the theoretical results.