This study presents a two-stage competent and efficient approach for optimal operation of wind–pumped-storage-hydro (PSH)–solar–thermal-storage hybrid power plant to get maximum system revenue and profit along with maintaining the grid frequency. The wind speed is predicted for a deregulated market and accordingly, the wind plants are committed to supplying the demand. The operation of PSH, battery and solar power are considered in order to minimise the adverse effect of imbalance cost which comes into the picture due to the mismatch between actual and predicted wind power. The proposed operating strategy for the complex hybrid plant helps to reduce the uncertainty of renewable power sources in an economical manner. Two new energy levels associated with pumped storage, i.e. PE_opt and PE_low and four energy levels associated with the battery, i.e. BE_max, BE_opt, BE_low and BE_min have been considered in this work to show the robustness of the proposed strategy. The proposed approach is implemented and compared using Mi-Power, bat algorithm, particle swarm optimisation algorithm, genetic algorithm and cuckoo search algorithm. Modified IEEE 14-bus system is used to validate the effectiveness of the proposed approach. The bilateral contracts with a double auction bidding model for the competitive power market are also considered for the implementation.

This study presents the dynamic and steady state stability improvement of a tidal power generation system (TPGS) assisted power system model (i.e. single machine infinite bus (SMIB) system) using a thyristor controlled series capacitor (TCSC). To achieve this objective, the TCSC used in the studied model is optimally designed using quasi-oppositional harmony search algorithm (QOHSA). The complete dynamic equation of the studied power system is realised based on d–q-axis decomposition. Eigenvalue analysis is performed using the frequency-domain approach to study the steady-state stability while the time-domain-based simulation is carried out to determine the dynamic stability of the studied system. To corroborate the effectiveness of QOHSA, results yielded by this algorithm are compared with those obtained using particle swarm optimisation technique. It may be inferred from the results obtained in this study that the QOHSA tuned TCSC outperforms the other in improving the overall stability of the TPGS-based SMIB system following disturbance. Furthermore, a similar analysis is extended to the TPGS-based multi-machine power system model to validate the efficiency of QOHSA in optimal designing of TCSC for dynamic stability improvement.