Design of hybrid energy systems using bat search optimization algorithm

This paper presents a framework for the optimal design of hybrid energy systems with a bat search optimization algorithm. Wind turbines, solar panels, and combined heat and power (CHP) systems are considered power generators. The proposed framework can exchange power with the upstream network, taking into account the limitations of power exchange, the cost of installing wind turbines, solar panels, CHP systems, battery storage, and converters in the form of investment costs plus maintenance costs, CHP system fuel costs, expected load removal costs, and power exchange costs as target functions. Also, a complete set of constraints related to the hybrid energy system is included in the problem framework. Uncertainties related to the production capacity of wind turbines and solar panels along with the uncertainty of consumer power demand using the two-point estimation method are also considered in the proposed framework. The bat search algorithm is used to find the optimal solution to the optimization problem. The results of the proposed framework in the form of different scenarios are compared with the results obtained by the genetic algorithm and particle swarm, which indicates its efficiency and optimal performance in designing hybrid energy systems.