Abstract
The rising number of connected devices, energy management, and local generation facilities implies that the consumers have an increased responsibility to the energy sector. Demand-side management (DSM) techniques involve the consumer in assisting grid services, delay of capital expenses, and utility revenue streams through several behind the metre services. A variety of literature studies have presented many techniques for the implementation of DSM in smart grid systems. This study emphasizes the modelling of DSM utilizing a day-ahead load shifting approach as a minimization problem. The DSM is modelled as an optimization problem whose solution is attained through a nature-based adaptive moth flame (AMF) technique. The formulated work has been tested on three demand zones: residential, commercial, and industrial, with diverse controllable loads. A comparison of solutions based on reduced peak demand and operational costs is undertaken with the proposed AMF optimization algorithm. Finally, it is shown that the DSM technique based on the AMF technique exhibits better savings than the multi-agent and evolutionary approach in the residential and commercial sectors. However, the particle swarm technique proves a better alternative than the proposed technique in achieving cost savings.
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Notes on contributors
Mohammad Zeeshan
Mohammad Zeeshan is a research scholar in the Department of Electrical Engineering, Jamia Millia Islamia, Delhi, India. He has done his masters with a specialization in control and instrumentation systems from Jamia Millia Islamia. His current research field is microgrid control and application of soft computing techniques in renewable applications.
Majid Jamil
Majid Jamil received his PhD from Jamia Millia Islamia, New Delhi & MSc Engineering (Power Systems & Drives) from Aligarh Muslim University, India. He published various papers in international & national Journals & conferences. His area of interest are electric power systems, Energy Management, Energy Auditing, transmission line protection, non-conventional energy, petri nets theory, fuzzy logic, power quality monitoring, and applications of expert systems E-mail: [email protected]