In California (CAISO), the duck curve [13; 14] indicates that the increasing trend in solar panel investment can be threatening to the system's reliability and efficiency for two reasons. First, renewable over-generation can compromise the system's safe dispatch due to uncertainty in generated power, lack of control, increased marginal prices (aging network congestion [15]), potential blackouts (2001 California crisis [16]), and negative marginal prices (renewable over-supply [17; 18; 19]). Second, ramping, i.e., sudden renewable production changes, can cause dispatching difficulties since conventional generators' ramping is limited and they have voltage support issues [20] due to the lack of sufficient reactive power in solar and most wind farms [20]. Similar problems have been observed or are expected to happen in other systems, such as in Irish systems [20], ERCOT, MISO, and NYISO [21]. With the aforementioned operational and investment issues related to renewable resources and battery storage in the CAISO and other US markets, the incorporation of the distributed load, i.e., commercial and residential buildings with a complex net load consisting of demand, local generation, and storage, in DR has been increasingly gaining interest for CO\({}_{2}\) emission reduction and grid operation support (by the Federal Energy Regulatory Commission, also known as FERC) [22]. As distributed loads may be a mix of different loads, battery storage, and generation, their contribution to CO\({}_{2}\) reduction, as well as grid flexibility, will be vital to the 2030 and 2050 goals. However, their large-scale integration via aggregators is difficult to implement due to the load mix complexity behind the meter, aggregator operations' scalability, prosumer privacy, and uncertainty quantification. Therefore, the need for aggregator operations with the aforementioned characteristics is immediate. Related Works: Although previous review papers have provided overviews of demand-response (DR) programs [23; 24; 25; 26; 27], DR frameworks [23; 26; 28], DR benefits for systems penetrated by renewable resources [24; 29; 30], ancillary services offered by DR [27; 28; 31], customer response to DR programs [24], DR pilot studies [26], demand-side resources [23] and methods [31], potential savings from energy management systems [32], blockchain technologies [33; 34; 35; 36], or local trading methods [37], a critical view on the role that distributed loads will play in future US electricity markets is missing not only in terms of energy savings, reliability, and flexibility but also in terms of the scalability of aggregator operations and the privacy of the prosumer in a future fully digitized smart grid. This review paper provides a holistic investigation of existing DR aggregator and prosumer technologies and frameworks, and proposes future directions for research that are compatible with five identified criteria. Limited related work exists in terms of critical review papers for game-theoretic frameworks on DR aggregators and distributed loads. For example, Ref. [38] covers a breadth of game-theoretic frameworks without proposing a unified evaluation framework to guide researchers on how to facilitate the large-scale and safe integration of distributed loads for the 2030 and 2050 goals. Our paper introduces and uses five evaluation criteria, including scalability, which evaluates the computational complexity of existing frameworks; this is something that is missing from [38], which primarily focuses on modeling. Other review papers in game theory with a scope broader than that of the DR aggregator exist [39; 40] but are not directly relevant. The contributions of this review are threefold. First, a thorough overview and evaluation of existing energy efficiency solutions and technologies for the distributed load are provided, including energy management systems and market-based DR via aggregators.