ELECTRICAL AND NON-ELECTRICAL ENERGY CONSUMPTION TREND IN A TYPICAL METROPOLIS (A SIMULATION BASED STUDY)

Abstract

Energy is essential to the functioning of an advanced industrial society. Alternative energy power systems can be designed to fulfill a great number of functions. The largest electric power loads in most households are the hot water heater, refrigerators and freezers, electric clothing dryers and ovens, and air conditioning. These appliances consume such huge amounts of electricity building a large enough renewable energy system to support them would not be cost effective. However for locations without access to grid power, or for long term reduction in electric bills, replacing these appliances with energy efficient alternatives will prove very cost effective in the long run. Energy efficiency and renewable energy are said to be the "twin pillars" of a sustainable energy policy. Renewable resources gained more attention during the last two decades because of the continues energy demand increasing parallel with fossil fuel resources decreases, additional to the environmental effect to the earth. Efficient energy use is essential to slowing the energy demand growth so that rising clean energy supplies can make deep cuts in fossil fuel use. The main objective of the present study is giving an overview about energy production, individual energy consumption, savings potential and also to find the optimum size of system, able to fulfill the energy requirements of a given load distribution, for site located at India and to analyze the impact of different parameters on the system size. In this context, an optimization sizing model is developed, based on the loss of power supply probability (LPSP) and the economical sub model based on the levelised cost of energy (LCE). The configuration with the lowest LCE gives the optimal one. Analyzing the optimal system configurations used to satisfy the requirements of typical residential home, the proposed system simulated using the MATLAB solver, the input parameters to the solver was the meteorological data for the selected locations and the sizes of the PV and the wind turbines. The results show that (sites) can use the solar and wind energy to generate enough power.