Present day power scenarios demand a high quality uninterrupted power supply and needs environmental issues to be addressed. Both concerns can be dealt with by the introduction of the renewable sources to the existing power system. Thus, Automatic Generation Control ( AGC ) with diverse renewable sources and a modified-cascaded controller are presented in the paper. Also, a new hybrid scheme of the Improved Teaching Learning Based Optimization-Differential Evolution ( hITLBO-DE ) algorithm is applied for providing optimization of controller parameters. A study of the system with a technique such as TLBO applied to a Proportional Integral Derivative ( PID ), Integral Double Derivative ( IDD ) and PIDD is compared to hITLBO-DE tuned cascaded controller with dynamic load change. The suggested methodology has been extensively applied to a 2-area system with a diverse source power system with various operation time non-linearities such as Dead-band of, Generation Rate Constraint and reheat thermal units. The multi-area system with reheat thermal plants, hydel plants and a unit of a wind-diesel combination is tested with the cascaded controller scheme with a different controller setting for each area. The variation of the load is taken within 1% to 5% of the connected load and robustness analysis is shown by modifying essential factors simultaneously by ∓ 30%. Finally, the proposed scheme of controller and optimization technique is also tested with a 5-equal area thermal system with non-linearities. The simulation results demonstrate the superiority of the proposed controller and algorithm under a dynamically changing load.

Bat algorithm is a recent addition to the bioinspired algorithms, considered as a new metaheuristic algorithm based on Bat behaviour. This work presents, the optimal solution of economic load dispatch (ELD) is obtained using the proposed bat algorithm. Here the operating cost of a thermal power plant is optimized using Bat algorithm. Numerical results show that the proposed method has good convergence property and better in quality of solution than PSO and IWD reported in recent literature. The main advantage of the proposed technique is easy is implement and capable of finding feasible near global optimal solution with less computational effort. BAT algorithm is easy to implement and priory in terms of accuracy and efficiency compared to other algorithms. In order to illustrate the effectiveness of the proposed method, it has been tested on 3 and 6-unit system.