Date of Award


Degree Name

Master of Science


Electrical and Computer Engineering

First Advisor



There is an increasing trend in the penetration of distributed generation into the structure of power systems. Under these circumstances, operator of deregulated electricity markets should systematically determine the operational schedule of distributed generation (DG) units in order to optimize the market profits and improve the network conditions. This thesis approaches the problem of daily scheduling of an energy market by taking into account not only the viewpoint of Distribution Company (Disco) but also the perspective of Environmental Protection Agency (EPA). Plus, an optimization method based on genetic algorithm (GA) is presented for the defined problem in order to address the different concerns of the electricity market accordingly. The presented algorithm for this task is called Adaptive Parallel Distributed GA (APDGA). In order to develop an effective connection among the GA chromosomes, a migration approach based on “parallel” computation is introduced. In addition, a “distributed” initialization technique is presented to replace the random initialization of classic GA and result in a more efficient first step for the algorithm which noticeably increases the convergence speed. Furthermore, a market framework is proposed in this thesis based on which the Disco collaborates with an EPA in order to bring about the active contribution of the owners of wind turbine DGs (WTDGs) and photovoltaic DGs (PVDGs) in the daily market. The presented optimization algorithm (i.e., APDGA) is systematically modeled in the proposed market framework in order to address the energy cost, system voltage drop, air pollution, and uncertainty of renewable resources. The effectiveness of the proposed model is demonstrated by its implementation on a 136-bus distribution system.




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