Performance evaluation of economic viability of a virtual power plant employing multiple nature inspired meta-heuristic techniques

The sudden shift towards generating power from renewable energy resources (RERs) is a growing interest in recent time. This transition will promote the use of clean and green energy sources but it will pose multiple challenges for the utility operators in managing these intermittent and unpredictable non-conventional sources especially, solar PV and wind. The continuous monitoring, control and management is anticipated by the independent system operator (ISO) which helps in scheduling the power to maintain the balance between supply and demand gap. A virtual power plant (VPP) is a key to deal with all these challenges in an effective manner in order to bring this paradigm shift to increase the share of renewable-based energy resources. This work addresses the scheduling and effectiveness of a VPP consisting of renewables, co-generating units, and storage reserve options to facilitate the power requirement in normal and emergency situations. The operator can also make some additional profit as the anticipated VPP network is inclined more towards profit making mechanism. To perform the optimal scheduling, a variety of multiple swarm intelligence techniques are utilized in this work which are metaheuristic algorithms enthused by nature. The obtained results are listed after repeated trials to ensure the accuracy of solutions. The solution sets are included in the numerical analysis and also linked with the existing work. It is found that the quality solution sets are effective in terms of improved convergence behavior and reduced elapsed time followed by significant improvement in the final outputs i.e., net profit and emission. The validation confirms the suitability and effectiveness of the selected techniques and outperforms the work available in the literature relevant to the proposed problem formulation.