Sensitivity analysis, optimal design, cost and energy efficiency study of a hybrid forecast model using HOMER pro

Abstract

Improved short-term wind forecasting for transmission system characteristics is needed to balance the power system with less stored capacity. Wind speed is studied using time-series analysis. Time series are repeated observations. Study of cost Energy efficiency reduces climate change, improves interior conditions, reduces air pollution to improve health, increases energy security, and determines cost-effectiveness for energy users. The hybrid optimization model for electrical renewables improves grid and off-grid electricity testing and design. It can model hybrid systems with generators, hydropower, wind turbines, batteries, solar PV, biomass, and fuel cells. Power engineers face challenges in identifying suitable energy sources, deciding the number of sources, and assessing and ensuring energy efficiency reductions. It optimizes and analyses system designs. This paper is split into 2 parts. Part 1 explains utilizing historical data to anticipate wind speed. The study examines an auto-regressive integrated moving average model, an artificial neural networks model, and a hybrid model for short-term wind speed prediction. The model's prediction accuracy considers root mean square error, mean absolute error and mean absolute percentage error. The hybrid model forecasts wind data best. The second section compares the energy efficiency of two microgrid models: an AC natural gas generator in model 1 and an AC diesel generator in model 2. This study uses a hybrid optimization model for electrical renewables pro to determine Jafrabad, Gujrat's most energy-efficient model. Two system setup examples were designed to find the best solution. In this article, projected wind data is supplied for two models as inputs of the wind turbine in the hybrid optimization model for electrical renewables pro to estimate energy cost, net present cost, and sensitivity analysis for both cases to assess microgrid changes due to solar radiation, wind speed, and fuel price. Pollutant/greenhouse gas emissions are also compared for both cases.