Distributed Optimization Strategy for Multi Area Economic Dispatch Based on Electro Search Optimization Algorithm
This work addresses the optimization of power generation costs across interconnected areas in electrical grids, which is an incremental improvement using a novel evolutionary algorithm for a known bottleneck in energy management.
The paper tackles the multi-area economic dispatch (MAED) problem, which is non-smooth, non-convex, and non-linear, by proposing a new electro search optimization algorithm (ESOA) that minimizes total cost while satisfying constraints like tie line capacity and prohibited operating zones, resulting in more accurate and robust performance compared to other methods.
A new adopted evolutionary algorithm is presented in this paper to solve the non-smooth, non-convex and non-linear multi-area economic dispatch (MAED). MAED includes some areas which contains its own power generation and loads. By transmitting the power from the area with lower cost to the area with higher cost, the total cost function can be minimized greatly. The tie line capacity, multi-fuel generator and the prohibited operating zones are satisfied in this study. In addition, a new algorithm based on electro search optimization algorithm (ESOA) is proposed to solve the MAED optimization problem with considering all the constraints. In ESOA algorithm all probable moving states for individuals to get away from or move towards the worst or best solution needs to be considered. To evaluate the performance of the ESOA algorithm, the algorithm is applied to both the original economic dispatch with 40 generator systems and the multi-area economic dispatch with 3 different systems such as: 6 generators in 2 areas; and 40 generators in 4 areas. It can be concluded that, ESOA algorithm is more accurate and robust in comparison with other methods.