PSO on renewable and non-renewable energy

Periasamy et al. (2022) researched the application of the particle swarm optimization (PSO) method in renewable and non-renewable energy sources. Their research aims to address issues related to costs, emissions, and financial burden sharing associated with different energy sources. They emphasize using hybrid algorithms and fuzzy systems to achieve these goals.

In addition, they discussed various techniques and mathematical modeling used in the PSO algorithm. These techniques and modeling can be used for more in-depth research in the future. They provided a thorough overview of how the PSO method can be applied to energy sources. They provided valuable insights on the use of optimization techniques to address challenges in renewable and non-renewable energy systems.

They concluded that the PSO method proves advantageous in solving multi-objective problems related to costs, emissions, and financial burden sharing in the context of renewable energy sources. This method is used to optimize various aspects such as cost, pollutant emissions, valve points, ramp rates, and constraints on generators. At the same time, fuzzy systems offer improved controllability, reliability, and efficiency compared to other controllers.

Various PSO methods investigation in renewable and nonrenewable sources

Madhumathi Periasamy, Thenmalar Kaliannan, Shobana Selvaraj, Veerasundaram Manickam, Sheela Androse Joseph, Johny Renoald Albert

Optimization structures are mostly considered for resolving multi-objective difficulties similar to cost, emission, and financial load dispatch in various energy sources. Non-renewable energy sources (NRES) emit harmful gases like CO2, and methane. which results in air pollutants, so various techniques are used in survey papers. By considering optimization techniques, the multi-objective problems are reduced in renewable energy sources (RES) and NRES. Implementing these techniques in RES and NRES will define the proper objective function. Hybrid algorithms are used for solving multi-objective problems like cost, pollutant emission, price penalty factor, valve point, ramp rates, and constraints like generator, power flow, power balance, and heat balance. A fuzzy system is used in numerous surveys for controlling purpose, superiority, and efficiency over other controllers. Subsequently summarized three types of sources like RES, RES-NRES, and NRES for easy identification of techniques and problems. This study reviews various techniques and mathematical modeling of algorithms for future research.

by: I. Busthomi