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Due to the increase in environmental concerns, it becomes mandatory now to focus on energy management and the planning of resources. An integration of distributed generation resources with conventional sources is gaining popularity. The hybrid modeling of the power system poses a complex and challenging optimization problem without getting trapped in a local optimum. A novel Hybrid Statistical Multiswarm Particle Swarm Optimizer-Sine Cosine Algorithm (HSMPSO-SCA) is developed in this paper, and its performance is evaluated on various test benchmark functions. The real operating conditions such as valve point loading, prohibited zones, ramp rate limits and uncertainties in solar and wind power generation for the IEEE 30 bus system are considered for combined emission economic load dispatch (CEELD). The performance of the HSMPSO-SCA is also analyzed for the solution of CEELD for various standard test systems with interaction of solar and wind plants. The efficacy of the proposed HSMPSO-SCA is found to be much better than various metaheuristics, hybrid and multi-agent algorithms in minimizing cost along with emissions optimally and achieves better accuracy and a faster convergence rate.

electric power components and systems

Combined Emission Economic Load Dispatch with Renewable Energy Sources Employing Hybrid Statistical Multiswarm Particle Swarm Optimizer-Sine Cosine Algorithm