Student Research

A Stackelberg Game Inspired Model of Real-Time Economic Dispatch with Demand Response


Smart grid applications and a drive towards greater consumer engagement are defining the evolution of the power system and its communication infrastructure. Bidirectional communication between the consumers and the operator allows the latter to impact the system’s overall consumption profile, through demand response (DR) programs

For optimal scheduling of generation units, this thesis presents a novel method for the operator to predict market prices and electrical load under a real-time pricing (RTP) demand response program in a microgrid. Inspired by the Stackelberg game, the proposed model establishes simulated trading between the network’s operator (leader) optimizing the generation cost and offering market prices to the customers (followers) who optimize their behavior. The model is formulated as a one-leader, N-follower iterative game where the optimization problems are solved using deterministic global optimization techniques. Simulations are performed on two microgrid systems where results show a significant improvement in the projected retail prices and electrical loads.

Additionally, this thesis also examines the impact of energy storage systems (ESS) on the operation of an industrial facility in RTP DR programs. A model is developed to optimally manage the energy storage and operation of the industrial load. Additionally, an approach to the sizing of the ESS is proposed. Stochastic modeling of electricity prices based on historical data is used to this end. The optimization models were tested on a generic industrial unit. Results show the benefits of ESS in increasing profit and highlight the impact of its installation cost on its feasibility.

The thesis work resulted in the following publications:

Project Details