Abstract

The curtailing of consumers’ peak hours demands and filling the gap caused by the mismatch between generation and utilization in power systems is a challenging task and also a very hot topic in the current research era. Researchers of the conventional power grid in the traditional power setup are confronting difficulties to figure out the above problem. Smart grid technology can handle these issues efficiently. In the smart grid, consumer demand can be efficiently managed and handled by employing demand-side management (DSM) algorithms. In general, DSM is an important element of smart grid technology. It can shape the consumers’ electricity demand curve according to the given load curve provided by the utilities/supplier. In this survey, we focused on DSM and potential applications of DSM in the smart grid. The review in this paper focuses on the research done over the last decade, to discuss the key concepts of DSM schemes employed for consumers’ demand management. We review DSM schemes under various categories, i.e., direct load reduction, load scheduling, DSM based on various pricing schemes, DSM based on optimization types, DSM based on various solution approaches, and home energy management based DSM. A comprehensive review of DSM performance metrics, optimization objectives, and solution methodologies is’ also provided in this survey. The role of distributed renewable energy resources (DERs) in achieving the optimization objectives and performance metrics is also revealed. The unpredictable nature of DERs and their impact on DSM are also exposed. The motivation of this paper is to contribute by providing a better understanding of DSM and the usage of DERs that can satisfy consumers’ electricity demand with efficient scheduling to achieve the performance metrics and optimization objectives.

Highlights

  • Electricity consumption is rising day by day and the existing conventional setup is experiencing difficulties in generating enough power to meet the current energy demands

  • The subsection classification of demand-side management (DSM) consists of direct load reduction, load scheduling, DSM based on energy pricing, DSM based on energy consumption scheduling, and DSM based on home energy management

  • Load scheduling is further classified into subclasses which include DSM based on energy pricing, DSM based on energy consumption scheduling, and DSM based on home energy management

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Summary

Introduction

Electricity consumption is rising day by day and the existing conventional setup is experiencing difficulties in generating enough power to meet the current energy demands. The energy demand further increases and providing the consumers with their demanded loads becomes more challenging. With the traditional power grid, it is very difficult to meet the above challenges [1]. To solve the issue of high consumer demand and the mismatch between generation and utilization, smart grid technology along with energy efficient scheduling is one of the solutions. We discuss smart grid technology along with efficient energy management procedures which can solve the above problem up to a certain level. The list of acronyms, notations, and symbols used throughout the paper are tabulated in Tables 1 and 2

Overview of the Smart Grid
Comparison between the Smart and Conventional Grids
Objective
Demand-Side Management and Demand Response Management
Contributions of This Survey
Paper Organization and Research Methodology
Demand-Side Management Classifications and Their Roles in Cost Minimization
Classification of DSM
Direct Load Reduction
Load Scheduling
Cost Minimization by Employing Efficient DSM Schemes
Game Theoretic and Real-Time Pricing Approaches in Cost Minimization
Integration of DERs
PAPR Minimization Using DERs
Role of DERs in Cost Minimization
Unpredictable Nature of DERs and the Impact on DSM
Future Research Directions
Reliability of the Power System and Communication Networks Used in DSM
Security and Privacy
Load Profile Modeling
Consumers’ Participation in the Proposed DSM Scheme
Consumers’ Preferences and Priorities
Maximization of DERs
Educating the Consumers
Conclusions
Findings
Methods and Optimization
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