Optimization Approaches for Demand-Side Management in the Smart Grid: A Systematic Mapping Study

Abstract

Demand-side management in the smart grid often consists of optimizing energy-related objective functions, with respect to variables, in the presence of constraints expressing electrical consumption habits. These functions are often related to the user’s electricity invoice (cost) or to the peak energy consumption (peak-to-average energy ratio), which can cause electrical network failure on a large scale. However, the growth in energy demand, especially in emerging countries, is causing a serious energy crisis. This is why several studies focus on these optimization approaches. To our knowledge, no article aims to collect and analyze the results of research on peak-to-average energy consumption ratio and cost optimization using a systematic reproducible method. Our goal is to fill this gap by presenting a systematic mapping study on the subject, spanning the last decade (2013–2022). The methodology used first consisted of searching digital libraries according to a specific search string (104 relevant studies out of 684). The next step relied on an analysis of the works (classified using 13 criteria) according to 5 research questions linked to algorithmic trends, energy source, building type, optimization objectives and pricing schemes. Some main results are the predominance of the genetic algorithms heuristics, an insufficient focus on renewable energy and storage systems, a bias in favor of residential buildings and a preference for real-time pricing schemes. The main conclusions are related to the promising hybridization between the genetic algorithms and swarm optimization approaches, as well as a greater integration of user preferences in the optimization. Moreover, there is a need for accurate renewable and storage models, as well as for broadening the optimization scope to other objectives such as CO2 emissions or communications load.

Integrated Demand Response Optimization for Consumer With Herd Mentality: A Genetic Simulated Annealing Approach

“…Peak-to-Average Ratio (PAR) is a critical indicator used for monitoring the difference between peak consumption and average usage in the context of energy systems. A lower PAR value indicates a more stable and reliable system, whereas a higher PAR value suggests imbalances in the production of electricity with cost implications [20], [21]. Therefore, a distributed Demand-Side Management (DSM) algorithm is proposed, which minimizes total energy costs and PAR by optimizing cumulative costs, thereby reducing PAR, total energy prices, and energy costs [22].…”

Electricity Demand Side Management

“…Finally, Mimi et al [25] performed a systematic review of optimization approaches for demand-side management in the Smart Grid, covering publications between 2013 and 2022, revealing the growing use of hybrid approaches involving meta-heuristics and identifying the need for more accurate models of renewable sources and storage elements.…”