A Residential Load Scheduling with the Integration of On-Site PV and Energy Storage Systems in Micro-Grid

Ihsan Ullah, Muhammad Babar Rasheed, Thamer Alquthami and Shahzadi Tayyaba
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Ihsan Ullah: Department of Computer Engineering, The University of Lahore, Lahore 54000, Pakistan
Muhammad Babar Rasheed: Department of Electronics and Electrical Systems, The University of Lahore, Lahore 54000, Pakistan
Thamer Alquthami: Electrical and Computer Engineering Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Shahzadi Tayyaba: Department of Computer Engineering, The University of Lahore, Lahore 54000, Pakistan

Sustainability, 2019, vol. 12, issue 1, 1-36

Abstract: The smart grid (SG) has emerged as a key enabling technology facilitating the integration of variable energy resources with the objective of load management and reduced carbon-dioxide (CO 2 ) emissions. However, dynamic load consumption trends and inherent intermittent nature of renewable generations may cause uncertainty in active resource management. Eventually, these uncertainties pose serious challenges to the energy management system. To address these challenges, this work establishes an efficient load scheduling scheme by jointly considering an on-site photo-voltaic (PV) system and an energy storage system (ESS). An optimum PV-site matching technique was used to optimally select the highest capacity and lowest cost PV module. Furthermore, the best-fit of PV array in regard with load is anticipated using least square method (LSM). Initially, the mathematical models of PV energy generation, consumption and ESS are presented along with load categorization through Zero and Finite shift methods. Then, the final problem is formulated as a multiobjective optimization problem which is solved by using the proposed Dijkstra algorithm (DA). The proposed algorithm quantifies day-ahead electricity market consumption cost, used energy mixes, curtailed load, and grid imbalances. However, to further analyse and compare the performance of proposed model, the results of the proposed algorithm are compared with the genetic algorithm (GA), binary particle swarm optimization (BPSO), and optimal pattern recognition algorithm (OPRA), respectively. Simulation results show that DA achieved 51.72% cost reduction when grid and renewable sources are used. Similarly, DA outperforms other algorithms in terms of maximum peak to average ratio (PAR) reduction, which is 10.22%.

Keywords: HEM; PV sizing; Load scheduling; Dijkstra Algorithm; BPSO; GA; optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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