A Micro Energy Grid Optimal Design and Economic Operation Using Genetic Algorithms

Abstract

This paper presents an optimal design procedure and economic operational scheduling of micro energy grids (MEGs). The optimization objectives are to minimize cost, carbon dioxide emissions, and energy deficiency. The energy sources and conversion technologies included in this study are renewable-based sources (wind and photovoltaic), a furnace, an electrical heater, a main power grid, and a local power station. Two proposed control levels are applied to control the operation of the MEG. The supervisor control level selects the energy supplier based on price and/or availability. The inner control level dynamically matches the demand profile with the supply profile. The control loops guarantee dynamic matching between the demand profile and supply profile. Two scenarios are simulated, zero interest rates and 5.25% interest rates. The results showed renewables contribute with a significant share as an energy source, however, higher interest rates would negatively impact this contribution. It also confirms that carbon taxes can reduce the use of fossil fuels as an energy source.