Day‐ahead optimal scheduling of microgrid with adaptive grasshopper optimization algorithm. (11th August 2019)

• Record Type: Journal Article
• Authors: C, Shilaja; T, Arunprasath; P, Priya
• Other Names: Raj Jennifer S. guestEditor.; Chen Joy Iong‐Zong guestEditor.; Kotuliak Ivan guestEditor.; Kamel Khaled guestEditor.
• Is Part Of: International journal of communication systems. Volume 35:Number 2(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-08-11
• Subjects: MG -- AGOA -- CS -- PV -- wind -- thermal -- DG -- fuel -- direct -- reserve -- penalty cost -- SOC -- IEEE 57 and 118‐bus system; Telecommunication systems -- Periodicals; 621.382
• DOI: 10.1002/dac.4133
• Languages: English
• ISSNs: 1074-5351

Abstract

Summary: Recently, the microgrid (MG) structure day‐ahead scheduling is an important aspect and achieved an optimal operation by maximizing the utility function. In this paper, a day‐ahead scheduling of MG and their optimal operation are analyzed with the help of the proposed adaptive algorithm. For the optimal analysis of MG, adaptive grasshopper algorithm (AGOA) with cuckoo search (CS) is proposed. The CS algorithm is utilized to update the learning functions of the GOA, and the optimal performances are evaluated. Here, the photovoltaic (PV), wind turbine (WT), battery, and diesel generator (DG) are considered to analyze the optimal scheduling issues, and the main aim is to minimize their generating and operational cost functions. In addition, to maximize the profit of operations in MG, the load demand must be satisfied according to their constraints and objectives. The multiobjective function is defined as the cost functions of MG such as the fuel cost, generation cost, state of charge (SOC), direct cost, reserve cost, and penalty cost, respectively. The proposed method is implemented in MATLAB/Simulink platform and tested with the IEEE 57‐bus system and IEEE 118‐bus system. In order to verify the effectiveness of the proposed method, this is compared with the existing methods such as whale optimization algorithm (WOA) and cuttlefish algorithm (CFA), respectively. Before the comparative study, the real‐time data of PV and WT are analyzed for the 24 hours. The SOC of the proposed method is analyzed and is about 80%. Abstract :