Design of Hybrid System Power Management Based on Load Demand Using Operational Control System
- DOI
- 10.2991/iccelst-st-19.2019.9How to use a DOI?
- Keywords
- operational control system, artificial neural network, hybrid system, load demand, energy management
- Abstract
Renewable energy is a limited energy source that involves of sunshine, wind, and water. It’s normally used as sources of renewable power plants. Despite of its advantages, these power plants also contribute some disadvantages, such as high generation costs, highly dynamic behavior, etc. The disadvantages are being raised due to instability of the energy sources (RER). This study is aimed to design power management of a hybrid system based on operational control system based Artificial Neural Network (ANN) based on load demand. In this study, Power Management of Hybrid System used 3 power plants: Photovoltaic (PV), Wind Power, and Micro Hydro Power Plant (MHPP), while Battery was employed as storage system. Main focus of the work was to determine the activation of each plant using ANN method to fulfill the load demand. Matlab Simulink was employed to develop and simulate the ANN on the system. From results of simulation it can be concluded that ANN can reach target accuracy level in around 80%. When the entire plant was interconnected, the ANN experienced a misreading due to the voltage drop in each generator that affected input of the ANN.
- Copyright
- © 2019, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Zulfatman Has AU - Fachmy Faizal AU - Nurhadi PY - 2019/12 DA - 2019/12 TI - Design of Hybrid System Power Management Based on Load Demand Using Operational Control System BT - Proceedings of the International Conference of CELSciTech 2019 - Science and Technology track (ICCELST-ST 2019) PB - Atlantis Press SP - 44 EP - 49 SN - 2352-5401 UR - https://doi.org/10.2991/iccelst-st-19.2019.9 DO - 10.2991/iccelst-st-19.2019.9 ID - Has2019/12 ER -