Wheeling using evolutionary programming based optimal power flow algorithm

Wheeling contracts are among the most important supply-options available to utility for generating least cost energy plans. Optimal power flows may be used to determine short term marginal cost and the best control settings to accommodate wheeling options so as to maintain system security while minimizing production cost. Classical optimal power flow algorithms suffer with different class of curves used to describe nonutility generator performance and very sensitive to starting points. Hence the proposed model handle wheeling contracts using an evolutionary programming based optimal power flow algorithm and makes the model partially free from the above said difficulties. Classical methods rely on convexity to obtain the global optimum solution. But unfortunately wheeling based on classical optimal power flow is, in general, nonconvex and, as a result, many local minima may exist. This nonconvexity once again increases for different types of generator options. In this paper, an attempt has been made to verify the validity of the proposed model using the IEEE 30 bus test system with and without modification. The results obtained are also compared with the classical techniques and it was found that the solutions obtained are quite encouraging.