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 i...

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Bibliographic Details
Authors: Montoya O.D., Gil-González W., Grisales-Noreña L.F., Orozco-Henao C., Serra F.
Format: article
Status:Published version
Publication Date:2019
Country:Colombia
Institution:Universidad Tecnológica de Bolívar
Repository:Repositorio Institucional UTB
Language:English
OAI Identifier:oai:repositorio.utb.edu.co:20.500.12585/9253
Online Access:https://hdl.handle.net/20.500.12585/9253
Access Level:Open access
Keyword: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
Description
Summary: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.

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