### Complexity of Distributed Load Mix in Future US Electricity Markets  
Prosumers (distributed load) may already have (or will have in the future) diverse load characteristics and, therefore, may require different contracts or pricing schemes (Figure 1 in [67]), some of which have been tested in pilot programs [31]. The prosumer's load synthesis is better explained by Figure 3. Five load sub-types are identified:  
1. Storable load (i.e., electric cars, batteries);
2. Shiftable load (i.e., dishwasher);
3. Curtailable load (i.e., lighting);
4. Self-generation (i.e., solar panels); \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Technology** & **Energy/CO\({}_{2}\) Savings** & **Equipment/Service** & **Paper** \\ \hline Efficient equipment & Varies & Appliances and devices & [49; 50] \\ Efficient lighting & Varies & Multiple technologies & [51] \\ Efficient HVAC & 4.5\% & Energy Star HVAC & [48] \\ Sensors & 55\% & Occupancy detection for HVAC & [43] \\  & 20\% & Sensing for light control & [52] \\ Energy management & Up to 23\% & Smart control per device & [32] \\ Efficient structures & 76.8\% & Polystyrene insulation & [53] \\  & 50–70\% & Passive solar design & [54] \\  & 3\% & Double skin facade (DSF) & [55] \\  & 60\% & DSF with solar and motor-blinds & [56] \\  & Varies & Green walls, shape, atrium architecture & [57; 58; 59] \\ Renewable integration & 62\% & Renewable energy supply & [44] \\ Utility-saving programs & 30–40\% of controllable load & Direct load control (DLC) & [60] \\  & 3.09\% & Conservation voltage reduction (CVR) & [61; 62; 63] \\  & 64\% & Irreducible/curtailable programs & [64] \\ Energy market & 3\% in 2004 & Peak load DR & [64] \\  & 42\% & 99\% time of usage/1\% critical peak pricing (CPP) & [65] \\  & 12–33\% & Real-time pricing & [66] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of energy-efficient technologies for the distributed load. Energy/CO\({}_{2}\) savings are shown as a percentage of the building’s total demand, calculated by multiplying the savings at the device level by the device’s participation in the total building’s demand. HVAC is estimated to consume about 50% of the total building’s demand [48]. Figure 2: Worldwide and US CO\({}_{2}\) emissions breakdown: (**a**) 2014 global CO\({}_{2}\) emissions from fossil fuel combustion and industrial processes according to US Department of Energy [46]; (**b**) total US CO\({}_{2}\) gas emissions emitted by economic sector, according to EPA (Table ES-6 in [47]) in 2020, with electricity-related emissions distributed by sector. The transportation sector is growing due to electric vehicles. Baseload, which is not curtailable and therefore not appropriate for DR [68]. The customer may choose if they wish to control their load independently via an energy management system or if they wish to transfer the control of their mixed load to the DR aggregator [69]. However, with the increasing penetration of renewable resources and storage in the load mix, the role of the DR aggregator has become more challenging, not only because of the millions of constraints but also due to the data coupling from a prosumer's meter. The customer's load mix shown in Figure 3 is measured as one net demand, and hence, existing forecasting methods for individual load sub-types do not apply [70]. The challenge of managing thousands/millions of prosumers with different load types, device constraints, demand and generation forecasting, and coupled behind-the-meter data creates the need for decentralized DR aggregator operations and for prosumers to schedule their own operations via an energy management system under the coordination of the DR aggregator while having more control over their own data privacy from local sensors and meters (open research questions on distributed load privacy, DR aggregators, and distributed load scalability are provided in Section 3.7).