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In recent years, the concept of Smart Grid has generated much attention among the producers and consumers of electric power, the policy makers, as well as the researchers. Smart Grid technology promises to revolutionize the way in which electricity is produced, delivered, and utilized. However, it requires technological advancement in a number of interdisciplinary domains before complete benefits of smart grid can be realized. Significant among them are the technological advances to enable substantial increase in the use of renewable energy sources coupled with a massive increase in energy efficiency in not only generation but also in distribution and utilization. In particular, the usage of renewable energy sources is envisioned to result into a massively distributed power generation and distribution system composed of a large number of generating stations operating on disparate renewable technologies. Optimal allocation of existing energy resources becomes a challenge due to massively distributed nature of generation facilities and consumption sites, and due to uncertainty caused by inherent random fluctuations in generation. In this paper, a Market Based technique has been presented for carrying out the optimal allocation for efficient utilization of the energy produced in a Smart Grid. The Market Based Resource Allocation is a distributed technique inspired by the concepts from the economic market where resources are allocated to the activities through the process of competitive buying and selling. In the proposed technique, the energy consumers act as the potential buyers of the energy and the energy producers act as the sellers of energy. The paper evaluates the proposed Market Based technique via a number of simulated scenarios of energy consumers and producers in a Smart Grid. The proposed technique optimizes the energy production cost and the transmission loss of electricity as these costs are reflected in the bidding and asking prices of the consumers and - - the sellers respectively.
American Control Conference (ACC), 2011
Date of Conference: June 29 2011-July 1 2011