Virtual Energy Storage-Based Energy Management Algorithm for Optimally Sized DC Nanogrid

Abstract

The optimal configuration of energy sources in a nanogrid is important due to intermittency of renewable energy sources and variability in load. An oversized configuration gives the maximum power supply availability to load. However, this would result in higher cost of electricity and excess power generation. Since, dc nanogrids are seldom connected to central grid, the surplus power cannot be sold back. On the other end, economical configuration leads to undersized system, i.e., insufficient generation to meet the load demand. Hence, in general, a multiobjective Grey-Wolf optimization-based optimal sizing algorithm is proposed to get optimal configuration for the area of photovoltaic (PV) panel installation, rating of the wind energy conversion system and capacity of battery energy storage system. Furthermore, there is need for real-time energy management in dc nanogrid due to uncertainty in power generation and load forecasting. A virtual energy storage (VES)-based energy management is proposed in this article to enhance the availability of power supply. The VES concept models the high thermal inertia loads such as air-conditioner and refrigerator analogous to electrochemical battery. A priority-based strategy in VES algorithm turn- <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</small> and turn- <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">off</small> thermal loads during the power deficit and surplus renewable generation receptively. The VES-based energy management does not compromise the comfort of customers and does not change the usual number of switching in thermal loads.