Distributed, Renewable, and Mobile Resources

Permanent URI for this collectionhttps://hdl.handle.net/10125/112468

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NEO-Grid: A Neural Approximation Framework for Optimization and Control in Distribution Grids
(2026-01-06) Fares El Hajj Chehade, Mohamad; Zhu, Hao
The rise of distributed energy resources (DERs) is reshaping modern distribution grids, introducing new challenges for maintaining voltage stability under dynamic and decentralized operating conditions. This paper presents NEO-Grid, a unified learning-based framework for volt-var optimization (VVO) and volt-var control (VVC) that leverages neural network surrogates for power flow and deep equilibrium models (DEQs) for closed-loop control. Our method replaces traditional linear approximations with piecewise-linear ReLU networks trained to capture the nonlinear relationship between power injections and voltage magnitudes. For control, we model the recursive interaction between voltage and inverter response using DEQs, allowing direct fixed-point computation and efficient training via implicit differentiation. We evaluate NEO-Grid on the IEEE 33-bus system, demonstrating that it significantly improves voltage regulation performance compared to standard linear and heuristic baselines in both optimization and control settings. Our results establish NEO-Grid as a scalable, accurate, and interpretable solution for learning-based voltage regulation in distribution grids.
Optimal Electricity Tariff Selection Considering Renewable Energy Sources, Electric Vehicles and Controllable Home Appliances
(2026-01-06) Ghanavati, Farideh; Matias, João C. O.; Osório, Gerardo J.; Catalão, João
This paper investigates the capabilities of a decision support system for optimal electricity tariff selection considering renewable energy sources, electric vehicles, and controllable home appliances. The developed model is based on a standard mixed-integer linear programming (MILP) approach, which considers different scenarios for a typical smart home. The tariff selection strategy is applied over a selected week to reflect varying electricity tariffs in Portugal. The model accounts for time-variant energy consumption at a 15-minute resolution, consistent with smart meter recordings and real-world electricity pricing structures. The proposed system enables residential users to make informed decisions by optimizing electricity costs while considering the flexibility of energy resources within smart homes. By simulating various operational scenarios, the model demonstrates the effectiveness of MILP in identifying the most cost-efficient tariff plans under dynamic consumption patterns. The results highlight the potential for such a tool to support energy cost reduction, improve demand-side management, and contribute to smarter energy usage.
An Appliance-Agnostic Mode Identification Framework via Dynamic Programming Least Squares and Piecewise Regression for Non-Intrusive Load Monitoring
(2026-01-06) Ostovar, Sara; Pudukkarai Srinivas, Nirupama; Khorsand, Mojdeh
Accurate appliance-level Load Monitoring (LM), particularly through Non-intrusive LM (NILM), is increasingly important for effective Demand-Side Management (DSM) in modern power systems. However, NILM techniques often struggle to achieve high disaggregation accuracy due to challenges in identifying underlying appliance operating modes. This paper presents an analytical, appliance-agnostic framework that integrates Dynamic Programming Least Squares (DPLS), piecewise linear regression, and rule-based clustering to robustly extract and characterize the transient signatures of appliances and estimate their associated mode characteristics especially the unique consumption trend inside each of the modes. Applied to granular sub-metered data from two representative refrigerators, the proposed method constructs concise mode dictionaries that capture key consumption behaviors—including steady-state, compressor transitions, and defrost events. The resulting structured mode dictionaries achieve over 98% variance explained (R^2), with the error of as low as 0.126 p.u., which can be readily adopted by NILM algorithms, improving disaggregation accuracy and generalization across appliance types.
Aggregate Modeling of Air-Conditioner Loads Under Packet-based Control with Both On and Off Grid Access Requests
(2026-01-06) Hassan, Mohammad; Almassalkhi, Mads
Coordination of distributed energy resources (DERs) can engender flexibility necessary to improve grid reliability. Packetized Energy Management (PEM) is a method for coordinating DERs, such as thermostatically controlled loads (TCLs) and electric vehicles, within customer quality-of-service (QoS) limits. In PEM, a DER uses local information to offer flexibility by sending a request to the DER coordinator to turn-ON or turn-OFF. Much work has focused on modeling and analyzing aggregations of DERs under PEM with fixed packet durations and only turn-ON requests. Different recent efforts to enable variable packet lengths have shown an increase in available flexibility and ramping capability, but have not been modeled in aggregate, which limits systematic analyses. To address this issue, this paper presents a new aggregate bin-based (macro) model of PEM loads that incorporates both turn-ON and turn-OFF request features, enabling the model to accurately characterize the capability of the fleet of DERs to track a power reference signal, population temperature dynamics, aggregate request rates, and variable packet lengths. Simulation-based validation is performed against an agent-based (micro) model to evaluate robustness and quantify model accuracy. Finally, the distribution of variable packet lengths from macro-model simulations are applied to inform past work on PEM with randomized packet lengths.
Multi-Stage Robust Optimization of a Wastewater Treatment Biogas Generator Participating in Day-Ahead and Real-Time Regulation Markets
(2026-01-06) Stuhlmacher, Anna; Kody, Alyssa
Wastewater treatment plants are large, energy-intensive loads that can be optimally controlled to support grid reliability and reduce operational costs. This paper presents a multi-stage robust optimization framework to control a wastewater treatment plant equipped with a biogas generator and storage tank for participation in California’s day-ahead and real-time frequency regulation markets while managing biogas production and regulation signal uncertainty. We solve for day-ahead regulation capacity in the first stage and real-time regulation capacity over the day as the uncertainty is progressively revealed. We propose affine control policies to determine the real-time regulation capacity based on partial uncertainty realizations. This results in a tractable affinely adjustable robust counterpart. In a case study, we evaluate our proposed approach against a day-ahead-only robust formulation and found that our approach increases regulation capacity provision and lowers operational costs.
Spatio-Temporal Energy Flexibility of Data Centers: Modeling the Impact on the Western Interconnection
(2026-01-06) Mirzaei, Mohammad Amin; Parvania, Masood
Data centers are rapidly becoming major electricity consumers due to the growth of digital infrastructure and AI, but they can transition from a grid challenge to a grid flexibility solution through workload scheduling, distributed power resources, and advanced cooling systems. This paper presents an analysis of the impacts of data center energy flexibility on the operation of the Western Interconnection. The energy flexibility of data centers is modeled through coordinated IT workloads with spatio-temporal flexibility, cooling systems enhanced with thermal energy storage, and integrated on-site energy resources including solar generation, small modular reactors, and battery storage systems. A multi-regional unit commitment framework is developed to integrate data center energy flexibility into the Western Interconnection while enforcing inter-regional transmission limits and operational constraints. The numerical results show that spatio-temporal flexibility of data centers significantly reduces the operation cost, while supporting a diverse generation mix in the Western Interconnection.
Introduction to the Minitrack on Distributed, Renewable, and Mobile Resources
(2026-01-06) Blumsack, Seth; Cardell, Judith

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