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

Byeong-Cheol Jeong, Dong-Hwan Shin, Jae-Beom Im, Jae-Young Park and Young-Jin Kim
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Byeong-Cheol Jeong: Department of Electrical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Gyungbuk 37673, Korea
Dong-Hwan Shin: Department of Electrical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Gyungbuk 37673, Korea
Jae-Beom Im: Department of Electrical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Gyungbuk 37673, Korea
Jae-Young Park: Department of Electrical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Gyungbuk 37673, Korea
Young-Jin Kim: Department of Electrical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Gyungbuk 37673, Korea

Energies, 2019, vol. 12, issue 6, 1-20

Abstract: 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.

Keywords: energy storage system (ESS); implementation; Internet of Things (IoT); microgrid (MG); microgrid energy management system (MEMS); mixed integer linear programming (MILP); open-source; optimal scheduling; two-stage (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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