### Energy Management Systems for Distributed Load Complexity Management  
Energy management systems can manage demands in distributed loads based on optimization algorithms; energy production forecasting from distributed resources, and appliances; and energy storage control [72]. For a distributed load that participates in the wholesale DR market through a DR aggregator [73; 74], such energy management systems can schedule appliance and device operations to match with the new demand bid submitted into the market. In other cases, the energy management system responds directly to price signals from the local utility [75]. The related terminology includes Home Energy Management Systems (HEMSs) for residential buildings and Building Energy Management Systems (BEMSs) for commercial buildings. These systems optimize the total operation cost,  
Figure 3: Distributed load mixture [71]. CHP stands for combined heat and power, and IT stands for information technology. and in some cases, they also maximize user satisfaction. For examples of implementations, see Table 2.  
Energy management systems utilize sensors that receive environmental, biomedical, or device usage signals and an associated controller that makes decisions to modify a device's demand so that an optimal result is achieved, such as energy efficiency, occupant's thermal comfort, occupant's visual comfort, or indoor air quality [83]. Table 3 summarizes sensing technologies and applications in building device control for energy savings. Energy management systems may also consider sensor input to schedule the day-ahead demand or to make demand modifications in real time. Specifically, when bidding into the day-ahead market (DAM), an initial demand schedule may be derived for the prosumer, but due to multiple uncertainties, this demand may slightly vary in real time when the day-ahead demand is realized. Therefore, real-time sensor inputs, such as the occupancy state of a room or building, may become real-time suggestions made in energy management systems that modify the operation of appliances, such as lights and HVAC [43], in order to avoid large deviations from the day-ahead demand bid and associated market costs and penalties; this is carried out if the real-time pricing (spot market) is higher than the day-ahead demand for some hour of the day and the prosumer demand has increased for that hour. The current literature in energy management systems for the distributed load does not consider integration with the prosumer-aggregator game via its pricing signals, as will be better explained in Section 3, and therefore, each prosumer's day-ahead demand bid is not tested for feasibility according to the prosumer's schedule, i.e., whether the demand bid is feasible and optimal for the prosumer's load mix (Section 2.2), leading to potential spot-market-imposed fines due to the day-ahead versus real-time demand discrepancies. This observation leads to the open research questions outlined in Section 3.7, which are related to the distributed load's HEMS/BEMS flexibility; its competition with the DR aggregator and other prosumers; uncertainty around the day-ahead demand considered by HEMS/BEMS; prosumer's preferences or personally identifiable (PI) information collected from the distributed load's HEMS/BEMS; and scalability when multiple device constraints, load types, and forecasting signals are present within HEMS/BEMS or when multiple distributed loads are integrated under the same DR aggregator via their HEMS/BEMS. The next section will introduce market-based DR programs via DR aggregators for the distributed load and emphasize the benefits of market participation for CO\({}_{2}\) emission reduction and grid reliability support.