arXiv subjects

Rajit Gadh

Publications and source records attributed to Rajit Gadh.

14 recordsLinked to original sources

arXiv2026

Optimal Engagement of Residential Battery Storage to Alleviate Grid Upgrades Caused by EVs and Solar Systems

The integration of distributed energy resources has ushered in a host of complex challenges, significantly impacting power quality in distribution networks. This work studies these challenges, exploring issues such as voltage fluctuations and escalating power losses caused by the integration of solar systems and electric vehicle (EV) chargers. We present a robust methodology focused on mitigating voltage deviations and power losses, emphasizing the allocation of a Permitted Percentage (PP) of battery-based solar systems within residential areas endowed with storage capabilities. A key facet of this research lies in its adaptability to the changing landscape of electric transportation. With the rapid increase of electric trucks on the horizon, our proposed model gains relevance. By tactically deploying PP to oversee the charging and discharging of batteries within residential solar systems, utilities are poised not only to assist with grid resilience but also to cater to the upcoming demands spurred by the advent of new EVs, notably trucks. To validate the efficacy of our proposed model, rigorous simulations were conducted using the IEEE 33-bus distribution network as a designed testbed. Leveraging advanced Particle Swarm Optimization techniques, we have deciphered the optimal charging and discharging commands issued by utilities to energy storage systems. The outcomes of these simulations help us understand the transformative potential of various PP allocations, shedding light on the balance between non-battery-based and battery-based solar residences. This research underscores the need for carefully crafted approaches in navigating the complexities of modern grid dynamics amid the anticipated increase in electric vehicles.

cs.AR↗

arXiv2025

Service-Oriented Fast Frequency Response from Flexible Loads and Energy Storage in Low-Inertia Power Systems

The increasing penetration of inverter-based renewable generation has significantly reduced system inertia, making modern power grids more vulnerable to rapid frequency deviations following disturbances. While a wide range of flexible resources-including electric vehicles (EVs), data centers, and battery energy storage systems (BESS)-have demonstrated the physical capability to provide fast frequency response (FFR), existing studies primarily focus on individual resource performance or controller-level designs. A systematic framework that translates heterogeneous FFR capabilities into deployable, system-level frequency services remains largely unexplored. This paper proposes a service-oriented coordination framework for fast frequency response from flexible loads and energy storage, bridging the gap between physical capability assessment and grid-operational utilization. The framework decomposes frequency support into multiple time-critical service layers based on response speed, power capacity, and energy sustainability, and dynamically allocates FFR responsibilities among heterogeneous resources accordingly. By explicitly accounting for response latency, saturation limits, and energy constraints, the proposed approach enables coordinated dispatch that prioritizes ultra-fast resources for initial frequency arrest while leveraging slower but energy-rich resources to sustain recovery.

eess.SY↗

arXiv2025

Fast Frequency Response Potential of Data Centers through Workload Modulation and UPS Coordination

The rapid growth of renewable energy sources has significantly reduced system inertia and increased the need for fast frequency response (FFR) in modern power systems. Data centers, as large and flexible electrical consumers, hold great potential to contribute to frequency stabilization due to their controllable IT workloads and on-site uninterruptible power supply (UPS) systems. This paper investigates the feasibility of leveraging data centers for providing fast frequency response through real-time workload modulation and UPS coordination. A dynamic model combining data center power consumption and grid frequency dynamics is developed, capturing the interactions between IT servers, cooling systems, and energy storage. Control strategies based on frequency deviation are implemented to adjust server power and discharge UPS batteries during frequency events. Case studies on a modified IEEE 39-bus system demonstrate that the proposed strategy can effectively reduce frequency nadir and shorten recovery time without compromising service quality. The results highlight the promising role of data centers as grid-supporting resources in future low-inertia systems.

eess.SY↗

arXiv2025

Coordinated Fast Frequency Response from Electric Vehicles, Data Centers, and Battery Energy Storage Systems

High renewable penetration has significantly reduced system inertia in modern power grids, increasing the need for fast frequency response (FFR) from distributed and non-traditional resources. While electric vehicles (EVs), data centers, and battery energy storage systems (BESS) have each demonstrated the capability to provide sub-second active power support, their combined frequency response potential has not been systematically evaluated. This paper proposes a coordinated control framework that aggregates these heterogeneous resources to provide fast, stable, and reliable FFR. Dynamic models for EV fleets, data center UPS and workload modulation, and BESS are developed, explicitly capturing their response times, power limits, and operational constraints. A hierarchical control architecture is introduced, where an upper-level coordinator dynamically allocates FFR among resources based on response speed and available capacity, and lower-level controllers implement the actual power response. Case studies based on the IEEE 39-bus test system demonstrate that the coordinated EV-DC-BESS framework improves frequency nadir by up to 0.2 Hz, reduces RoCoF, and accelerates frequency recovery compared with single-resource FFR. Results confirm that synergistic coordination significantly enhances grid stability, especially in low-inertia scenarios. This work highlights the value of multi-resource aggregation for future frequency regulation markets in renewable-dominated grids.

eess.SY↗

arXiv2025

Assessing the Frequency Response Potential of Heavy-Duty Electric Vehicles with Vehicle-to-Grid Integration in the California Power System

The integration of heavy-duty electric vehicles (EVs) with Vehicle-to-Grid (V2G) capability can enhance primary frequency response and improve stability in power systems with high renewable penetration. This study evaluates the technical potential of heavy-duty EV fleets to support the California power grid under three practical charging strategies: immediate charging, delayed charging, and constant-minimum-power charging. We develop a simulation framework that couples aggregated frequency dynamics with battery and charger constraints, state-of-charge management, and fleet-availability profiles. Performance is assessed using standard frequency security metrics, including nadir, rate-of-change-of-frequency, overshoot, and settling time, across credible contingency scenarios and renewable generation conditions. Results indicate that both non-V2G modes and V2G-enabled operation can contribute meaningful primary response, with V2G providing the strongest and fastest support while respecting mobility and network limits. Sensitivity analyses show that the relative benefits depend on charging strategy, control parameters, and renewable output, highlighting design trade-offs between response magnitude, duration, and battery usage. Overall, heavy-duty EV fleets-when coordinated by appropriate charging and V2G controls-offer a viable resource for strengthening primary frequency control on the California grid and mitigating stability challenges associated with increasing renewable penetration.

eess.SY↗

arXiv2025

Heavy-Duty Electric Vehicles Contribution for Frequency Response in Power Systems with V2G

The integration of heavy-duty electric vehicles (EVs) with Vehicle-to-Grid (V2G) capability offers a promising solution to enhance grid stability by providing primary frequency response in power systems. This paper investigates the potential of heavy-duty EVs to support the California power grid under different charging strategies: immediate, delayed, and constant minimum power charging. Simulation results demonstrate that both V2G-capable EVs and non-V2G modes have great potential to provide primary frequency response, with V2G-capable EVs exhibiting especially strong contributions. The study highlights the influence of charging strategies, control modes, and grid conditions on EV contributions to grid stability, emphasizing their critical role in mitigating the adverse effects of renewable energy penetration.

eess.SY↗

arXiv2019

Hierarchical Distributed EV Charging Scheduling in Distribution Grids

In this paper, a hierarchical distributed method consisting of two iterative procedures is proposed for optimal electric vehicle charging scheduling (EVCS) in the distribution grids. In the proposed method, the distribution system operator (DSO) aims at reducing the grid loss while satisfying the power flow constraints. This is achieved by a consensus-based iterative procedure with the EV aggregators (Aggs) located in the grid buses. The goal of aggregators, which are equipped with the battery energy storage (BES), is to reduce their electricity cost by optimal control of BES and EVs. As Aggs' optimization problem increases dimensionally by increasing the number of EVs, they solved their problem through another iterative procedure with their customers. This procedure is implementable by exploiting the mathematical properties of the problem and rewriting Aggs' optimization problem as the \textit{sharing problem}, which is solved efficiently by the alternating direction method of multipliers (ADMM). To validate the performance, the proposed method is applied to IEEE-13 bus system.

math.OC↗

arXiv2019

Hierarchical Distributed Framework for EV Charging Scheduling Using Exchange Problem

In this paper, a distributed trilayer multi-agent framework is proposed for optimal electric vehicle charging scheduling (EVCS). The framework reduces the negative effects of electric vehicle charging demand on the electrical grids. To solve the scheduling problem, a novel hierarchical distributed EV charging scheduling (HDEVCS) is developed as the \textit{exchange problem}, where the agents are clustered based on their coupling constraints. According to the separability of the agents' objectives and the clusters' coupled constraints, HDEVCS is solved efficiently in a distributed manner by the alternating direction method of multipliers. Comparing to the exiting trilayer methods, HDEVCS reduces the convergence time and the iteration numbers since its structure allows the agents to update their primal optimization variable simultaneously. The performance of HDEVCS is evaluated by numerical simulation of two small- and large- scale case studies consisting of $306$ and $9051$ agents, respectively. The results verify the scalability and efficiency of the proposed method, as it reduces the convergence time and iteration numbers by $60\%$ compared to the state-of-the-art methods, flattens the load profile and decreases the charging cost considerably without violating the grid feeders' capacity. The significant outcome of our method is the accommodation of a large EV population without investment in grid expansion.

math.OC↗

arXiv2018

Electric Vehicle Driver Clustering using Statistical Model and Machine Learning

Electric Vehicle (EV) is playing a significant role in the distribution energy management systems since the power consumption level of the EVs is much higher than the other regular home appliances. The randomness of the EV driver behaviors make the optimal charging or discharging scheduling even more difficult due to the uncertain charging session parameters. To minimize the impact of behavioral uncertainties, it is critical to develop effective methods to predict EV load for smart EV energy management. Using the EV smart charging infrastructures on UCLA campus and city of Santa Monica as testbeds, we have collected real-world datasets of EV charging behaviors, based on which we proposed an EV user modeling technique which combines statistical analysis and machine learning approaches. Specifically, unsupervised clustering algorithm, and multilayer perceptron are applied to historical charging record to make the day-ahead EV parking and load prediction. Experimental results with cross-validation show that our model can achieve good performance for charging control scheduling and online EV load forecasting.

cs.LG↗

arXiv2017

Real-Time Bi-directional Electric Vehicle Charging Control with Distribution Grid Implementation

As electric vehicle (EV) adoption is growing year after year, there is no doubt that EVs will occupy a significant portion of transporting vehicle in the near future. Although EVs have benefits for environment, large amount of un-coordinated EV charging will affect the power grid and degrade power quality. To alleviate negative effects of EV charging load and turn them to opportunities, a decentralized real-time control algorithm is developed in this paper to provide optimal scheduling of EV bi-directional charging. To evaluate the performance of the proposed algorithm, numerical simulation is performed based on real-world EV user data, and power flow analysis is carried out to show how the proposed algorithm improve power grid steady state operation. . The results show that the implementation of proposed algorithm can effectively coordinate bi-directional charging by 30% peak load shaving, more than 2% of voltage drop reduction, and 40% transmission line current decrease.

math.OC↗

arXiv2017

Distributed Optimal Vehicle Grid Integration Strategy with User Behavior Prediction

With the increasing of electric vehicle (EV) adoption in recent years, the impact of EV charging activities to the power grid becomes more and more significant. In this article, an optimal scheduling algorithm which combines smart EV charging and V2G gird service is developed to integrate EVs into power grid as distributed energy resources, with improved system cost performance. Specifically, an optimization problem is formulated and solved at each EV charging station according to control signal from aggregated control center and user charging behavior prediction by mean estimation and linear regression. The control center collects distributed optimization results and updates the control signal, periodically. The iteration continues until it converges to optimal scheduling. Experimental result shows this algorithm helps fill the valley and shave the peak in electric load profiles within a microgrid, while the energy demand of individual driver can be satisfied.

math.OC↗

arXiv2016

Optimal Operation of Stationary and Mobile Batteries in Distribution Grids

The trending integrations of Battery Energy Storage System (BESS, stationary battery) and Electric Vehicles (EV, mobile battery) to distribution grids call for advanced Demand Side Management (DSM) technique that addresses the scalability concerns of the system and stochastic availabilities of EVs. Towards this goal, a stochastic DSM is proposed to capture the uncertainties in EVs. Numerical approximation is then used to make the problem tractable. To accelerate the computational speed, the proposed DSM is tightly relaxed to a convex form using second-order cone programming. Furthermore, in light of the continuous increasing problem size, a distributed method with a guaranteed convergence is applied to shift the centralized computational burden to distributed controllers. To verify the proposed DSM, real-life EV data collected on UCLA campus is used to test the proposed DSM in an IEEE benchmark test system. Numerical results demonstrate the correctness and merits of the proposed approach.

eess.SY↗

arXiv2015

Two-Tier Prediction of Solar Power Generation with Limited Sensing Resource

This paper considers a typical solar installations scenario with limited sensing resources. In the literature, there exist either day-ahead solar generation prediction methods with limited accuracy, or high accuracy short timescale methods that are not suitable for applications requiring longer term prediction. We propose a two-tier (global-tier and local-tier) prediction method to improve accuracy for long term (24 hour) solar generation prediction using only the historical power data. In global-tier, we examine two popular heuristic methods: weighted k-Nearest Neighbors (k-NN) and Neural Network (NN). In local-tier, the global-tier results are adaptively updated using real-time analytical residual analysis. The proposed method is validated using the UCLA Microgrid with 35kW of solar generation capacity. Experimental results show that the proposed two-tier prediction method achieves higher accuracy compared to day-ahead predictions while providing the same prediction length. The difference in the overall prediction performance using either weighted k-NN based or NN based in the global-tier are carefully discussed and reasoned. Case studies with a typical sunny day and a cloudy day are carried out to demonstrate the effectiveness of the proposed two-tier predictions.

stat.AP↗

arXiv2013

Design of Fast Response Smart Electric Vehicle Charging Infrastructure

The response time of the smart electrical vehicle (EV) charging infrastructure is the key index of the system performance. The traffic between the smart EV charging station and the control center dominates the response time of the smart charging stations. To accelerate the response of the smart EV charging station, there is a need for a technology that collects the information locally and relays it to the control center periodically. To reduce the traffic between the smart EV charger and the control center, a Power Information Collector (PIC), capable of collecting all the meters power information in the charging station, is proposed and implemented in this paper. The response time is further reduced by pushing the power information to the control center. Thus, a fast response smart EV charging infrastructure is achieved to handle the shortage of energy in the local grid.

eess.SY↗