### Energy Management Systems for Distributed Load Complexity Management
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**ISO** & \multicolumn{2}{c}{**Total DR Load**} & \multicolumn{2}{c}{**DR as \% of Peak Demand**} \\  & **2010** & **2022** & **2010** & **2022** \\ \hline CAISO & 2135 MW & 3900 MW & 4.5\% & 36\% \\ ERCOT & 1484 MW & 4354.5 MW & 2.3\% & 5.9\% \\ ISO-NE & 2116 MW & 533.7 MW & 7.8\% & 2.3\% \\ MISO & 8663 MW & 12,197 MW & 8\% & 10.2\% \\ NYISO & 2498 MW & 1345.5 MW & 7.5\% & 4.4\% \\ PJM & 13,306 MW & 9914 MW & 10.5\% & 6.8\% \\ SPP & 1500 MW & 176.2 MW & 3.3\% & 0.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: DR participation across different ISOs in the US in 2010 [107] and in 2022 [108]. The available DR is used as a capacity resource and for DR emergencies, ancillary services, and bidding energy. Develop protocols and contracts for customer participation [71]. * Develop the software for managing all of the above. The DR aggregator may be a retailer or have a close connection to a retailer [71; 123]. Retailers perform load forecasting, assess fluctuations in customer demand, and provide new pricing schemes [124], services that are similar to the DR aggregator [125]. On the other hand, the DR aggregator communicates with system operators, such as the distribution system operator (DSO), if it exists, and the ISO. The DSO can provide access to the DR aggregator with customers' demand data [126], and the ISO is the buyer of services from transmission and distribution resources [70; 127]. Case studies have been performed before to evaluate the effectiveness of DR, including Table 5. In a University of Pennsylvania case study [128], the building's \"DR-Advisor\" tool showed that with a data-driven DR tool, 17% better curtailment performance can be achieved compared to rule-based strategies (380 kW load curtailment saving over USD 45,000 in one event). In another study by the US Department of Energy [129], 13 different DR types were characterized across sectors according to the frequency and duration of response metrics, enabling costs, etc., and it was found that the cooling load types in residential and commercial prosumers had the least ability to take advantage of energy arbitrage opportunities across US states in the Western Interconnection, while data centers and water heating showed moderate capability, with municipal pumping experiencing the highest ability. CAISO in another voluntary DR study [130] defined a metric for evaluating the response time and ramping capability of DR across 200 K residential buildings and found the period of responses to vary within \(-18\%\) and \(3\%\) from the event's time, while the response varied with income and other demographics. Another study by OSTI, Department of Energy [131], showed that aggregate DR and battery optimization can reduce the load peak by 2.9% (or 26 MW) during the summer of 2017 in Austin, TX. Lastly, another Department of Energy study [132] framed DR as a co-equal flexibility resource alongside storage. The same study provided materials for future case studies as they emphasized the need for modeling innovation to accurately represent aggregated DR behavior via DR aggregators, while they called attention to regulatory and data hurdles that also plague DR participation and DR valuation. The same study mentions that inconsistent DR participation rules, aggregation limits, inadequate metering/communication, and unclear compensation mechanisms are key obstacles to DR valuation in general. The traditional operation of DR aggregators provides them with access to information about each distributed load with a load mix, as explained in Section 2.2, which includes knowledge of the prosumer's devices and models; the prosumer's demand patterns, behaviors, and preferences (potentially PI data); the prosumer's work and house occupancy schedule; individual device demand forecasting for devices without models; generation forecasting; operations of battery storage or electric vehicles; etc.