## 1 Introduction
### Research Gap and Originality Highlights
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **DR Type** & **Study Scale** & **Methodology** & **Achievements** \\ \hline \multirow{4}{*}{[31]} & Hybrid price-based DR (HPDR) & Residential micro-grid & Day-ahead HPDR scheduling to a residential micro-grid, considering uncertainty related to generation and dispatch. & In comparison to ToU and RTP and fixed-rate (FR) pricing, HPDR, which is a combination of ToU and RTP, showed a lower decrement in the peak-to-valley index (PtV) by 12\% and Coefficient of variation percentage (CVP) by 25\% and increased social welfare by 18\%. \\ \cline{2-5}  & \multirow{4}{*}{ToU} & Residential household & DR strategies (rule-based and machine-learning (ML) based) for controlling a heat pump and thermal storage system in a smart-grid ready residential household. & The proposed ML prediction-based smart controller under a ToU DR scheme showed superior performance reducing electricity end-use usage, utility generation cost, and carbon emission by 41.8\%, 39\%, and 37.9\%, respectively. \\ \hline \multirow{4}{*}{[33]} & Dynamic price-based & Residential local energy market & Agent-based simulations for pricing strategy and demand shifting strategy under dynamic pricing DR are applied on a local electricity market (LEM) based on the German energy market. & The model was simulated for a LEM with 100 households, increasing its local sufficiency by 16\% through DR and local Trading, showing a 10 c\&/kWh reduction in annual electricity costs as well as 40\% reduced peaks. \\ \hline \multirow{4}{*}{[34]} & \multirow{4}{*}{ToU} & \multirow{4}{*}{5000 residential households} & DR strategies, using 18 months of data in Ireland where different ToU schemes were applied to 5000 households coupled with information feeding (in-home display units (HHD), monthly billing, etc.) & The ToU DR coupled with the information feed reduced energy demands for the given household, especially in peak demand periods. However, after implementation, little DR impact was observed by changing the elasticance between the peak and off-peak prices. \\ \hline \multirow{4}{*}{[35]} & Dynamic price-based & Two residential buildings & OpenStudio and EnergyPlus to assess the effects of DR potential on HVAC systems through changing temperature set points in two residential buildings in Texas, USA & In applying two different types of real-time dynamic tariff pricing, simulation results showed that a reduction potential of 10.8\% in energy costs could be achieved through the proposed DR controller without significant impact on comfort levels and savings of 24.7\% peak load and 4.3\% of energy for HVAC could be achieved annually. \\ \hline \multirow{4}{*}{[36]} & Dynamic price-based & Residential household & Dynamic price-based DR by modeling the optimal consumer response through fuzzy reasoning (FR) and reinforcement Learning (RL) & Simulation results showed by considering consumer preferences through morning and evening demand peak periods. \\ \hline \multirow{4}{*}{[37]} & \multirow{4}{*}{ToU and incentive-based} & Simulation of two DR systems: (1) an augmented ToU DR system solved using a stochastic optimal load aggregation model. (2) An incentive-based DR is solved using a two-stage stochastic unit commitment (UC) model satisfying operational cost reduction and consumer convenience. & Simulations for one load aggregator and 100 residential households showed that under augmented ToU DR with 60\% consumer participation level, generation costs can be reduced by 24\% and load profiles’ standard deviation by 42\%. Although with a higher 60\% consumer participation level reaching 80\%, the model becomes less efficient. Although under the incentive-based DR, 77\% standard deviation and 20\% generation costs reductions can be achieved. \\ \hline \hline \end{tabular}
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Table 1: Recent studies on Residential DR systems.