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1. Baseline Estimation and Scheduling for Demand Response

   https://doi.org/10.1109/CDC.2018.8619236  

   Muthirayan, Deepan; Kalathil, Dileep; Poolla, Kameshwar; Varaiya, Pravin (December 2018, 2018 IEEE Conference on Decision and Control (CDC))

   null (Ed.)

   Demand Response (DR) programs serve to reduce the demand for electricity at times when the supply is scarce and expensive. Consumers or agents with flexible consumption profiles are recruited by an aggregator who manages the DR program. These agents are paid for reducing their energy consumption from contractually established baselines. Baselines are counter-factual consumption estimates against which load reductions are measured. Baseline consumption and the true cost of load reduction are consumer specific parameters and are unknown to the aggregator. The key components of any DR program are: (a) establishing a baseline against which demand reduction is measured, (b) designing the payment scheme for agents who reduce their consumption from this baseline, and (c) the selection scheme. We propose a self-reported baseline mechanism (SRBM) for the DR program. We show that truthful reporting of baseline and marginal utility is both incentive compatible and individually rational for every consumer under SRBM. We also give a a pod-sorting algorithm based DR scheduling for selecting consumers that is nearly optimal in terms of expected cost of DR provision. 

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2. Urban scale vehicle-to-building-to-grid integration leveraging human mobility modeling for enhanced grid flexibility

   https://doi.org/10.1007/s12273-025-1346-3  

   Liu, Yapan; Dong, Bing (November 2025, Building Simulation)

   Abstract Across the U.S., the increasing demand for electric vehicle (EV) charging infrastructure is placing new demands on the power grid, challenging its stability and efficiency. To address these challenges, this study proposes a Vehicle-to-Building-to-Grid (V2B2G) framework that incorporates urban-scale human mobility modeling to optimize EV charging and discharging. Using anonymized GPS traces from the study community, we extracted individual mobility patterns and applied kernel-density estimation to predict departure and arrival times for each user. The framework was tested in a mixed-use community in Phoenix, Arizona that includes both residential and commercial buildings. A comprehensive decentralized model predictive control (MPC) framework is implemented to minimize energy costs and enhance grid flexibility through demand-side management while maintaining occupant comfort. Four different control strategies were designed and evaluated, the strategy which balances both user and grid benefits demonstrated the best performance, achieving: (1) a flattened grid net load curve, with a 56% reduction in on-peak demand and a 56% decrease in peak load rebound; (2) a 37.96% reduction in grid net load compared to the baseline control; and (3) a 68.05% performance improvement when considering six flexibility factors: cost savings, total-energy reduction, on-peak demand reduction, off-peak demand reduction, load shifting from peak to non-peak hours, and peak-load-rebound reduction. These findings enhance our understanding of the impacts of urban mobility and EV charging optimization on grid management. 

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3. Residential electricity demand on CAISO Flex Alert days: a case study of voluntary emergency demand response programs

   https://doi.org/10.1088/2753-3751/ad0fda  

   Peplinski, McKenna; Sanders, Kelly T (December 2023, Environmental Research: Energy)

   Abstract The California Independent System Operator (CAISO) utilizes a system-wide, voluntary demand response (DR) tool, called the Flex Alert program, designed to reduce energy usage during peak hours, particularly on hot summer afternoons when surges in electricity demand threaten to exceed available generation resources. However, the few analyses on the efficacy of CAISO Flex Alerts have produced inconsistent results and do not investigate how participation varies across sectors, regions, population demographics, or time. Evaluating the efficacy of DR tools is difficult as there is no ground truth in terms of what demand would have been in the absence of the DR event. Thus, we first define two metrics that to evaluate how responsive customers were to Flex Alerts, including theFlex Period Response, which estimates how much demand was shifted away from the Flex Alert period, and theRamping Response, which estimates changes in demand during the first hour of the Flex Alert period. We then analyze the hourly load response of the residential sector, based on ∼200 000 unique homes, on 17 Flex Alert days during the period spanning 2015–2020 across the Southern California Edison (SCE) utility’s territory and compare it to total SCE load. We find that the Flex Period Response varied across Flex Alert days for both the residential (−18% to +3%) and total SCE load (−7% to +4%) and is more dependent on but less correlated with temperature for the residential load than total SCE load. We also find that responsiveness varied across subpopulations (e.g. high-income, high-demand customers are more responsive) and census tracts, implying that some households have more load flexibility during Flex Alerts than others. The variability in customer engagement suggests that customer participation in this type of program is not reliable, particularly on extreme heat days, highlighting a shortcoming in unincentivized, voluntary DR programs. 

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4. Tromino: Demand and DRF Aware Multi-Tenant Queue Manager for Apache Mesos Cluster

   https://doi.org/10.1109/UCC.2018.00015  

   Saha, Pankaj; Beltre, Angel; Govindaraju, Madhusudhan (January 2019, 2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC))

   Apache Mesos, a two-level resource scheduler, provides resource sharing across multiple users in a multi-tenant clustered environment. Computational resources (i.e., CPU, memory, disk, etc.) are distributed according to the Dominant Resource Fairness (DRF) policy. Mesos frameworks (users) receive resources based on their current usage and are responsible for scheduling their tasks within the allocation. We have observed that multiple frameworks can cause fairness imbalance in a multi-user environment. For example, a greedy framework consuming more than its fair share of resources can deny resource fairness to others. The user with the least Dominant Share is considered first by the DRF module to get its resource allocation. However, the default DRF implementation, in Apache Mesos' Master allocation module, does not consider the overall resource demands of the tasks in the queue for each user/framework. This lack of awareness can lead to poor performance as users without any pending task may receive more resource offers, and users with a queue of pending tasks can starve due to their high dominant shares. In a multi-tenant environment, the characteristics of frameworks and workloads must be understood by cluster managers to be able to define fairness based on not only resource share but also resource demand and queue wait time. We have developed a policy driven queue manager, Tromino, for an Apache Mesos cluster where tasks for individual frameworks can be scheduled based on each framework's overall resource demands and current resource consumption. Dominant Share and demand awareness of Tromino and scheduling based on these attributes can reduce (1) the impact of unfairness due to a framework specific configuration, and (2) unfair waiting time due to higher resource demand in a pending task queue. In the best case, Tromino can significantly reduce the average waiting time of a framework by using the proposed Demand-DRF aware policy. 

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5. Causal relationship discovery and causal‐oriented approaches for enhanced performance and interpretability in prediction of prosumer behavior and demand flexibility

   https://doi.org/10.1049/gtd2.13319  

   He, Mingyue; Khorsand, Mojdeh (October 2024, IET Generation, Transmission & Distribution)

   Causal analysis paves the way for more interpretable assessment of complex systems and phenomena, such as human‐in‐the‐loop components of energy systems. This article will pursue novel approaches for causal analysis of prosumers' behavior. The knowledge of this causality is core for multiple smart grid applications including but not limited to the design of demand side management programs, retail electricity market design, development of effective distributed energy resources aggregation strategies, and net load forecasting. The complex nature of human interactions with energy relies on many factors and understanding behavior causality is a core, unsolved challenge. This article presents a probabilistic algorithm for discovering causal relationships between the end users' consumption flexibility and its influencing factors. The obtained causal knowledge is then utilized to boost the precision of demand flexibility prediction. Two causal‐oriented approaches are proposed to enhance the performance and interpretability of predictive models, incorporating causal information through causal regularization and data preprocessing. Simulation results demonstrate that the algorithm can effectively identify causal probabilities among different factors and unveil key characteristics of the prosumers' behavior. Additionally, these proposed causal‐oriented approaches outperform the non‐causal‐oriented predictive models in terms of both performance and interpretability, highlighting the advantages of incorporating causal information. 

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