Abstract
This paper addresses the optimal dispatch problem for battery energy storage systems (BESSs) in direct current (DC) mode for an operational period of 24 h. The problem is represented by a nonlinear programming (NLP) model that was formulated using an exponential voltage-dependent load model, which is the main contribution of this paper. An artificial neural network was employed for the short-term prediction of available renewable energy from wind and photovoltaic sources. The NLP model was solved by using the general algebraic modeling system (GAMS) to implement a 30-node test feeder composed of four renewable generators and three batteries. Simulation results demonstrate that the cost reduction for a daily operation is drastically affected by the operating conditions of the BESS, as well as the type of load model used.
Subject
Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous)