Economic Dispatch of BESS and renewable generators in DC microgrids using voltage-dependent load models

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. © 2019 MDPI AG. All rights reserved.

• Fecha de publicación: 2019
• Institución: Universidad Tecnológica de Bolívar
• Repositorio: Repositorio Institucional UTB
• Idioma: eng
• OAI Identifier: oai:repositorio.utb.edu.co:20.500.12585/9253
• Acceso en línea: https://hdl.handle.net/20.500.12585/9253
• Palabra clave: Artificial neural networks; Battery energy storage system; Economic dispatch problem; Battery storage; Cost reduction; Data storage equipment; Electric batteries; Electric machine theory; Neural networks; Nonlinear programming; Scheduling; Battery energy storage systems; Economic dispatch problems; Operating condition; Operational periods; Photovoltaic sources; Renewable generators; Short term prediction; Voltage dependent load models; Electric load dispatching
• Rights: openAccess
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/