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

Abstract

The ever increasing demand for electricity and the rapid increase in the number of automatic electrical appliances have posed a critical energy management challenge for both utilities and consumers. Substantial work has been reported on the Home Energy Management System (HEMS) but to the best of our knowledge, there is no single review highlighting all recent and past developments on Demand Side Management (DSM) and HEMS altogether. The purpose of each study is to raise user comfort, load scheduling, energy minimization, or economic dispatch problem. Researchers have proposed different soft computing and optimization techniques to address the challenge, but still it seems to be a pressing issue. This paper presents a comprehensive review of research on DSM strategies to identify the challenging perspectives for future study. We have described DSM strategies, their deployment and communication technologies. The application of soft computing techniques such as Fuzzy Logic (FL), Artificial Neural Network (ANN), and Evolutionary Computation (EC) is discussed to deal with energy consumption minimization and scheduling problems. Different optimization-based DSM approaches are also reviewed. We have also reviewed the practical aspects of DSM implementation for smart energy management.

DC Nanogrids for Integration of Demand Response and Electric Vehicle Charging Infrastructures: Appraisal, Optimal Scheduling and Analysis

“…It is realistic to assume that charging and discharging processes are actually complementary, which is ensured by imposing the constraint (10). Equation ( 11) models the state of charge of the batteries, which is limited by their nominal capacity and depth of discharge settings [40], as said the Equation (12).…”

“…Communication channels enable effective information exchange among the different assets that participate in the operation of the system. This novel paradigm allows to effectively coordinate the participation of the different agents, with the aim of pursuing more efficient management of the generation and storage facilities [11]; while consumers can also actively participate through DR initiatives, modifying their consumption profiles on the basis of price or incentive signals [12].…”

Optimized Economic Load Dispatch with Multiple Fuels and Valve-Point Effects Using Hybrid Genetic–Artificial Fish Swarm Algorithm

“…However, a regular change in the basic fuel price level plays the role of emphasizing the problem and enhancing its economic significance. Inflation also poses problems in developing economic operational techniques, methods, and examples of power generation systems [6]. Moreover, the rapid increase in the size and power demand of power systems resulted in reduced operating costs while maintaining the thermal limitations of voltage security and transmission line branching.…”

“…In the most common formulation, the ELD problem is modeled as a large-scale, non-convex, nonlinear, static optimization problem in both discrete and continuous control variables [9]. Many researchers [6][7][8][9] have modeled the nonlinear, convex nature of ELD problems using pure quadratic functions, with the quadratic coefficients (a, b, and c) defined at the start of the solution search process. Meanwhile, research work [10] developed realistic models to incorporate the effect of multiple fuel cost functions and valve-point loading into the formulation of the ELD problem.…”

A Digital Twin Architecture Model Applied with MLOps Techniques to Improve Short-Term Energy Consumption Prediction

“…A smart grid enables bidirectional communication between utilities and consumers, which may be used to optimize energy usage by demand side management (i.e., demandresponse). As increasing energy demand and peak of energy consumption are concerns for utilities, the demand side management enables an effective method to reduce costs of electricity, which in turn restrict the need for more investments in transmission and distribution infrastructure [1,2].…”