# An Enhanced System Architecture for Optimized Demand Side Management in Smart Grid
## 2 Related Work  
Various pricing strategies have been designed for users' energy cost minimization [13]. For instance, a day ahead pricing scheme has been used in [14] for appliance scheduling in order to minimize total energy cost. Authors in [15] proposed a Linear Programming (LP) model to optimize the home energy consumption and total cost. In this model, a full day is divided into equal length time slots with changing prices just like Time of Use (ToU) tariff. A Dynamic Demand Response Controller (DDRC) has been designed in [16]. The purpose of DDRC is to control the Heating Ventilation Air Conditioning (HVAC) unit according to Real-Time Pricing (RTP) signals. The controller considers thermal properties of construction materials in order to calculate the thermal insulation and losses. DDRC switches between cooling and heating modes on the basis of threshold temperature and has been tested on a single family house model. DDRC is limited to the HVAC only, and we have presented a cost minimization model with three load categories and multiple scheduling options. Authors in [17] have proposed a distributed DSM model in which users are equipped with energy storage facilities. Game theory has been applied in the proposed model to set the energy consumption and the storage game. Distributed algorithms ensure users' privacy and cost minimization. A Model Predictive Control (MPC) framework has been proposed for peak load shaving by optimal use of storage batteries in [18]. Markov process and an \"update\" algorithm have been used to predict a user's location and update the energy matrix accordingly. This model considers the single user location for prediction of energy consumption. Our proposed model, CHEMA, considers a multiple number of users present in different time slots to control lighting load and hot water consumption. A system architecture has been presented in [19], which includes three layers. The first layer is the named Admission Controller (AC), and it uses the modified spring algorithm for management of requests of appliances. The second layer is used for optimization of home energy management and is called the Load Balancer (LB). Mix integer programming is used at this layer to generate the optimized schedule for appliances. The third layer is proposed for the smart grid interface and has not been implemented in the scheme. We have proposed a new CHEMA model, which consists of six layers and multiple energy management options for users. CHEMA is based on enhanced load categories, multiple scheduling options and flexible user comfort index with relaxed temperature limits. Unique features of CHEMA include: use of multiple cost minimization options with a single Knapsack technique with inclusion of partial base line load in optimization problem. The same model includesthe theft and faults detection capability along with calculations of green effects at residential level. The model also takes account of the number of users present at home to control the partial base line load and hot water consumption. Inclusion of these features make CHEMA a unique comprehensive model. On the basis of the discussion above, we have included the following features in CHEMA. The proposed system architecture in [19] has three layers. However, there are certain aspects regarding HEMS, which should be included in the HEMS architecture to make it more comprehensive, _i.e._, inclusion of green effects, detection of theft and faults at residential level and incorporation of multiple pricing schemes in order to use it at different occasions. Generally, three categories of appliances have been used in literature: base line load, regular load and burst load. Base line load consists of essential loads such as fans, lighting, _etc._ This type of load can be included into schedulable load on partial or half basis. The definition of thermal comfort zone instead of sharp threshold to switch on/off the thermal loads is also helpful in cost minimization and peak load reduction.