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

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%.

Impact of the Lockdown during the COVID-19 Pandemic on Electricity Use by Residential Users

“…It necessitates the proposition of models that minimise the electricity cost and user discomfort while taking into account the peak energy consumption. For residential DSRs, approaches based on Artificial Intelligence (AI) are used across the Internet of Things (IoT), e.g., [60,61].…”

A Comprehensive Review on Residential Demand Side Management Strategies in Smart Grid Environment

“…Besides, it incorporated charge and discharge schedules of energy storage and EV [127]. In [128], a load scheduling problem was solved via the Dijkstra algorithm, and the simulation results are compared with GA, Optimal Pattern Recognition Algorithm, and BPSO. The results showed a cost reduction of about 51%.…”

Optimal Operation of a Hybrid Power System as an Island Microgrid in South-Korea

“…The charging mode (pump/motor unit) is calculated by Equation (12). The generator cannot operate in charging mode.…”

“…Thus, the coordination and cooperation of various 2 of 18 viable energy storage devices should be considered. In recent years, energy storage devices have been used in conjunction with renewable energy sources to reduce the peak load, alleviate output fluctuations, provide an emergency power supply, and facilitate energy prosumers [11][12][13][14][15][16]. There are various types of energy storage systems (ESSs): mechanical storage systems include pumped hydroelectric systems (PHS), compressed air energy storage (CAES), and flywheels; other types of ESSs have lead-acid, lithium-ion, and fuel cells [17].…”