Discrete Multiobjective Grey Wolf Algorithm Based Optimal Sizing and Sensitivity Analysis of PV-Wind-Battery System for Rural Telecom Towers

Abstract

Conventionally, base stations of off-grid telecommunication (telecom) towers are powered by diesel generator, which results in high operating cost and carbon emission. Hence, the photovoltaic (PV) wind battery based hybrid renewable energy system (HRES) is proposed for off-grid telecom towers in rural areas. However, owing to high capital cost and intermittent nature of renewable energy resources, optimal sizing of HRES is inevitable to have economical and reliable power supply. An index named excess energy generation (EEG) is developed to reduce the surplus renewable energy, which cannot be sold to grid in off-grid towers. Therefore, three objective functions are formulated viz., loss of power-supply probability, cost of electricity, and EEG, which are incommensurate and contradictory in nature. Thus, a discrete multiobjective grey wolf optimization (DMGWO) algorithm is developed for the optimal sizing of HRES. DMGWO utilizes the concept of internal archive to store nondominated solutions and enables fast convergence rate without premature termination as a result of inherent leader selection mechanism. A practical approach is adopted in optimal sizing by considering wind capacity as discrete variable, which is handled by the corrective algorithm embedded in DMGWO. The intermittency in source and variability in telecom load is incorporated using probability distribution functions. The Euclidean distance approach is adopted to find the solution (configuration of PV panels, wind turbines, and battery) from the optimal Pareto front obtained from DMGWO. Also, sensitivity analysis is carried out to observe the effect of the design variables on the objective functions.