Implementation of Optimal Two-Stage Scheduling of Energy Storage System Based on Big-Data-Driven Forecasting—An Actual Case Study in a Campus Microgrid

Optimal operation scheduling of energy storage systems (ESSs) has been considered as an effective way to cope with uncertainties arising in modern grid operation such as the inherent intermittency of the renewable energy sources (RESs) and load variations. This paper proposes a scheduling algorithm...

Full description

Saved in:
Bibliographic Details
Published inEnergies (Basel) Vol. 12; no. 6; p. 1124
Main Authors Jeong, Byeong-Cheol, Shin, Dong-Hwan, Im, Jae-Beom, Park, Jae-Young, Kim, Young-Jin
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 22.03.2019
Subjects
Algorithms
Alternative energy sources
Case studies
Costs
Distributed generation
Energy management
Energy resources
Energy storage
energy storage system (ESS)
Forecasting
implementation
Internet of Things (IoT)
Linear programming
Load
microgrid (MG)
microgrid energy management system (MEMS)
mixed integer linear programming (MILP)
open-source
optimal scheduling
Optimization
Real time
Renewable resources
Schedules
Scheduling
Software
two-stage
Online AccessGet full text

Cover

More Information
Summary:Optimal operation scheduling of energy storage systems (ESSs) has been considered as an effective way to cope with uncertainties arising in modern grid operation such as the inherent intermittency of the renewable energy sources (RESs) and load variations. This paper proposes a scheduling algorithm where ESS power inputs are optimally determined to minimize the microgrid (MG) operation cost. The proposed algorithm consists of two stages. In the first stage, hourly schedules during a day are optimized one day in advance with the objective of minimizing the operating cost. In the second stage, the optimal schedule obtained from the first stage is repeatedly updated every 5 min during the day of operation to compensate for the uncertainties in load demand and RES output power. The ESS model is developed considering operating efficiencies and then incorporated in mixed integer linear programming (MILP). Penalty functions are also considered to acquire feasible optimal solutions even under large forecasting errors in RES generation and load variation. The proposed algorithm is verified in a campus MG, implemented using ESSs and photovoltaic (PV) arrays. The field test results are obtained using open-source software and then compared with those acquired using commercial software.
ISSN:1996-1073
1996-1073
DOI:10.3390/en12061124