## 3 Demand-Response Aggregator Frameworks in Future US Electricity Markets
### Open Research Questions for Future Demand-Response Aggregators' Operations
Decentralized solutions that do not require the DR aggregator to solve the prosumer's problem, but rather only receivethe optimal solution, are preferred for this purpose. Such approaches facilitate the 2030 and 2050 goals according to Section 1's points 6 and 9. * **Scalability (S)** with the number of prosumers, as well as with the number and type of prosumer constraints, despite the complex nature of the distributed load mix: Ideally, every prosumer would be represented by a detailed HEMS/BEMS system and be given controls to facilitate flexibility, but at the cost of computational complexity. Methods like the solution decentralization of DR aggregator and prosumer operations (i.e, avoid solving a large, single-level optimization problem of the DR aggregator and the prosumers' KKT conditions), parallelization, and approximate algorithms can be introduced to manage these computational challenges. Scalability is important to the 2030 and 2050 goals because the large-scale integration of flexible generation and demand to the grid via DR can defer system expansion costs and alleviate the system's stress according to Section 1's points 6 and 8. To allow for the aforementioned characteristics to co-exist in the same prosumer-aggregator framework, researchers and policymakers are encouraged to consider the literature gaps addressed in Section 3 and to find ways to accelerate the distributed load participation in the DR markets (data sharing policies, information provided by ISO/DSO to DR aggregators, carbon pricing, etc.). In future research, the aforementioned characteristics can be addressed by considering flexible constraints (inequalities instead of equalities, a range of possible demand scenarios, etc. ); hierarchical or decentralized algorithms that solve the framework (including blockchains); game-theoretic frameworks that address prosumer  
Figure 4: This diagram shows the key players in DR markets and indicates potential interactions between them with arrows. The different necessary frameworks are displayed on the left, and they are enabled by the discussed technologies in this paper. Finally, the operational challenges are depicted in blue, which pose the open research questions examined in this paper, and they are important for the 2030 electricity generation decarbonization and 2050 net-zero-economy goals. aggregator competition according to the mechanism designed by the DR aggregator; RL and multi-agent RL, MPC, and Bayesian games; robust, distributionally robust optimization, or stochastic optimization frameworks that can address uncertainty; and finally, privacy preservation operations that hide prosumer-specific information and PI data from the DR aggregator while still allowing all the above characteristics to exist (i.e., decentralized and private learning, also known as federated learning, differential privacy, homomorphic encryption, and secure multiparty computation). The challenging task of integrating all of the above characteristics in the same framework and algorithm will allow for the large-scale deployment and flexible operation of DR aggregators and distributed loads in future US electricity markets while preserving customer privacy, a concern of increasing amplitude to the Department of Energy. Following the gap in prosumer DR aggregator game-theoretic frameworks, it is not only necessary that prosumers are able to implement their total demand schedule bid on their devices, but they should also be able ensure that demand uncertainties can be corrected in real time to avoid market fines. Energy-saving solutions at the distributed load level include sensor-based automation for smart device control, as well as energy management systems that optimize the building's operation based on price signals by a utility or aggregator. Therefore, the direction of integrating the prosumer-aggregator game with the prosumer's energy management system in the same algorithmic solution and software can facilitate flexibility, scalability, competition, uncertainty, and privacy.