Assessing the Techno-Economic Benefits of Flexible Demand Resources Scheduling for Renewable Energy–Based Smart Microgrid Planning

Mark Kipngetich Kiptoo, Oludamilare Bode Adewuyi, Mohammed Elsayed Lotfy, Theophilus Amara, Keifa Vamba Konneh and Tomonobu Senjyu
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Mark Kipngetich Kiptoo: Graduate School of Science and Engineering, University of the Ryukyus, Okinawa 903-0213, Japan
Oludamilare Bode Adewuyi: Graduate School of Science and Engineering, University of the Ryukyus, Okinawa 903-0213, Japan
Mohammed Elsayed Lotfy: Graduate School of Science and Engineering, University of the Ryukyus, Okinawa 903-0213, Japan
Theophilus Amara: Graduate School of Science and Engineering, University of the Ryukyus, Okinawa 903-0213, Japan
Keifa Vamba Konneh: Graduate School of Science and Engineering, University of the Ryukyus, Okinawa 903-0213, Japan
Tomonobu Senjyu: Graduate School of Science and Engineering, University of the Ryukyus, Okinawa 903-0213, Japan

Future Internet, 2019, vol. 11, issue 10, 1-16

Abstract: The need for innovative pathways for future zero-emission and sustainable power development has recently accelerated the uptake of variable renewable energy resources (VREs). However, integration of VREs such as photovoltaic and wind generators requires the right approaches to design and operational planning towards coping with the fluctuating outputs. This paper investigates the technical and economic prospects of scheduling flexible demand resources (FDRs) in optimal configuration planning of VRE-based microgrids. The proposed demand-side management (DSM) strategy considers short-term power generation forecast to efficiently schedule the FDRs ahead of time in order to minimize the gap between generation and load demand. The objective is to determine the optimal size of the battery energy storage, photovoltaic and wind systems at minimum total investment costs. Two simulation scenarios, without and with the consideration of DSM, were investigated. The random forest algorithm implemented on scikit-learn python environment is utilized for short-term power prediction, and mixed integer linear programming (MILP) on MATLAB ® is used for optimum configuration optimization. From the simulation results obtained here, the application of FDR scheduling resulted in a significant cost saving of investment costs. Moreover, the proposed approach demonstrated the effectiveness of the FDR in minimizing the mismatch between the generation and load demand.

Keywords: wind turbine (WT); flexible demand resources (FDR); battery energy storage system (BESS); demand side management (DSM); solar photovoltaic (PV); random forest (RF) (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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