## 1 Introduction
### Literature Review  
DR systems can be mainly classified into time-based and incentive-based pricings as well as other subcategories such as direct load control (DLC), demand bidding (DB), emergency DR programs (EDRP), time-of-use (ToU), real-time pricing (RTP), and critical peak pricing (CPP) [19; 20]. [PERSON] et al. introduced a DR model considering interruptible/curtailable loads as well as capacity market programs where they combined the price elasticity of demand model with the customer benefit. Their model applied to the Iranian power grid peak load data can aid GOs in improving the power load curve, while considering customer well-being [21]. [PERSON] et al. introduced a mathematical model for incentive-based and time-based DR programs to consider the correct balance of penalty and incentive rates to achieve DR's best performance for given demand levels [22]. [PERSON] et al. discussed observations based on psychology and economy that consumers react differently to both incentive (reward) and price (punishment) based DR systems which are not considered in conventional DR models [18]. [PERSON] et al. proposed an improvement to the price elasticity matrix of demand (PEMD) to unify the modeling of rigid and flexible loads by introducing a weighting factor and measured the effect of price policies and load types on calculating the elasticity matrix [23]. [PERSON] et al. implemented an optimal strategy for both bidding and scheduling for aggregators of distributed energy resources (DER) who manage distributed solar and wind energies and battery storage systems under uncertain consumer response to real-time pricing (RTP) considering uncertainties of renewable energy generation and consumer response [24]. [PERSON] et al. presented an optimization model that considers the combination of direct load control demand response and economic dispatch under renewable obligation policies, where the model maintains a predefined renewable energy share in the mix of different energy sources [25]. [PERSON] et al. proposed a demand response modelling approach for increasing the efficiency of renewable energy deployment, which considers operational improvements and a system planning perspective. Their model captures the correlation of uncertain variables such as renewable energy generation, customer demand and changing responsiveness to DR and utilizes clustering methods to implement a scenario reduction that reduces the computational complexity of the model [26]. [PERSON] and [PERSON] formulated a modeling approach to optimally implement an incentive-based demand response to match the variations of electrical demand and supply. Their model is coupled with a focus on assessing voluntary-based consumer-centric demand response systems that are less complex and cost-effective to other methods [27].  
The employment of DR systems depends on precise knowledge of future wholesale electricity market prices and customer demands and future renewable energy generation. Hence, researchers have combined both machine learning algorithms and DR models to overcome  
Figure 2: Standard supply–demand curve of electrical energy markets.  
these challenges. [PERSON] and [PERSON] proposed a novel real-time incentive-based DR system that utilized both deep learning and reinforcement learning (RL) algorithms combining two deep neural networks (DNNs) for day ahead price and demand predictions [28]. [PERSON] et al. implemented a modified deep learning (MDL) algorithm to predict 24-ahead power demand, prices, and PV energy generation in incentive-based demand response models utilizing recurrent neural networks (RNN) architectures [29]. [PERSON] et al. presented an improvement in short-term load forecasting for DR systems using an ensemble of two DL approaches; convolutional neural networks (CNN) and long short-term memory (LSTM)-RNN, showing higher performance over conventional models [30]. Table 1 shows recent DR studies systems on multiple scales of the residential sector using different DR schemes.