A new intelligent approach for size optimization of a renewable energy based grid connected hybrid energy system

Abstract

Adopting hybrid power systems to provide access to clean, reliable, and inexpensive energy is necessary for countries like India to accomplish their goals of sustainable development. This work proposes an optimization approach for sizing a grid‐connected hybrid renewable energy system (HRES) that includes photovoltaic, biomass, biogas, and a battery. In this research, a recently developed Aquila optimization algorithm is utilized to evaluate the optimal size of HRES for a cluster of villages near Sarai Jairam, Agra district, Uttar Pradesh, India using the MATLAB software package. The goal of this optimization approach is to reduce the net present cost for HRES while satisfying the operational constraints, including the reliability of the power supply and maximum utilization of the photovoltaic, biomass, and biogas complementary properties. Initially, three HRES configurations in the off‐grid mode were optimized using the suggested Aquila optimization technique. To confirm the findings, the optimization results for the same models of off‐grid HRES were obtained using the harmony search (HS) and particle swarm optimization (PSO) algorithms. The same algorithms were also used to optimize the grid‐connected SPV/biomass/biogas with the battery model of HRES. Finally, the outcomes from the off‐grid models presented above were compared to the grid‐linked model. The grid‐connected configuration which is obtained by the Aquila optimization approach provides an optimal solution with the least net present cos t (NPC) and minimum cost of energy (COE) when compared with HS and PSO. The optimal grid‐connected HRES includes 235 kW of PV, a 10 kW biogas generator, a 64 kW biomass generator, and a 50.40 kWh battery bank. The overall net present cost and the COE are found to be $547 670 and $0.0768/kWh, respectively.

Modelling and Optimisation of Green Energy Systems Based on Complementary Integration of Renewable Energy, 1-9.

“…Sharma et al proposed an optimisation method for hybrid power system, and used Aquila algorithm and particle swarm optimisation algorithm (PSO) to obtain the model optimisation results. This method can provide the optimal solution with the minimum energy cost [12]. Li et al proposed a modelling and MOO method for an independent photovoltaic wind turbine hydrogen battery hybrid energy system based on hysteresis bands.…”

“…The user's electricity demand, internal system power consumption, and operational energy consumption of air source heat pumps at time t are E t user , E t p , and E t hp . The constraint conditions for gas load balance are (12).…”

“…In (12), the purification amount of biogas at t is B t clean . The gas storage capacity of the step before t is B t−∆t con .…”