arXiv subjects

Vineet Jagadeesan Nair

Publications and source records attributed to Vineet Jagadeesan Nair.

15 recordsLinked to original sources

arXiv2026

Nuclear fusion for AI: A pathway to power data centers sustainably

This perspective examines whether nuclear fusion can provide a scalable, low-carbon power source for rapidly growing AI-driven data center demand. As large language models, cloud computing, and cryptocurrency mining accelerate electricity consumption growth, data centers are projected to account for a substantially larger share of U.S. and global electricity use in the coming decades, creating significant pressure on grid reliability and decarbonization goals. We evaluate the technical and economic alignment between data center load profiles and nuclear power, particularly fusion, through a comparative analysis of capacity factors, levelized cost of electricity, grid interconnection constraints, and deployment pathways. Unlike intermittent renewables, nuclear fission and fusion offer high-capacity-factor, firm baseload generation suited to AI training and inference workloads that require continuous, reliable power. Preliminary techno-economic analysis suggests that several Nth-of-a-kind fusion concepts, particularly magnetic confinement systems, may become cost-competitive with firmed renewable systems and advanced fission for hyperscale data center applications. Co-location of fusion plants with data centers further reduces transmission bottlenecks, improves resilience, and aligns with emerging hyperscaler procurement strategies. We also assess recent regulatory developments and argue that fusion's favorable safety profile and reduced waste burden improve its long-term social and political viability relative to fission. We conclude that fusion represents a strategically important pathway for sustainably powering next-generation computing infrastructure and should be prioritized in both policy and industrial deployment planning.

eess.SY↗

arXiv2026

Understanding the role and value of battery storage in renewables-rich distribution grids under uncertainty and different market paradigms

Economy-wide electrification is driving steep load growth, while renewables and batteries are increasing pressure at the distribution grid edge often requiring costly upgrades. Local Electricity Markets (LEMs) can coordinate distributed energy resources (DERs) more efficiently than conventional designs. This paper extends the LEM framework by modeling photovoltaic (PV) inflow as an Ornstein-Uhlenbeck (OU) process to capture uncertainty and temporal correlation. Using rolling-horizon alternating optimal power flow on the IEEE 123-node feeder, we compare operator, customer, and system costs plus storage value under three market designs: flat tariff (FT), real-time pricing (RTP), and LEM. Results show that the LEM achieves the lowest customer and system costs, under uncertainty, while RTP performs worst. It outperforms the other markets across most storage and solar penetration levels. However, it may worsen customer outcomes at very low PV penetration, indicating that LEM adoption should accompany PV expansion and precede storage expansion. We examine two approaches to estimating battery storage value. Storage value depends on storage penetration, imbalance between storage and PV penetrations, location, and whether batteries are colocated with renewables and/or load. Poorly placed units can worsen congestion, whereas well-sited units benefit owners and the system. Both the marginal value of new storage and the stored energy value of existing capacity decline as PV penetration rises. However, they respond oppositely to battery penetration: marginal value falls because of diminishing returns as the fleet grows, while stored energy value rises as more batteries compete for the same local PV generation. These metrics capture different dimensions of storage value under a LEM: one measures the benefit of expanding the fleet, while the other measures the scarcity premium on energy already stored.

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arXiv2026

Multiobjective optimization-based design and dispatch of islanded, hybrid microgrids for remote, off-grid communities in sub-Saharan Africa

Reliable, affordable electricity remains inaccessible to over 600 million people in sub-Saharan Africa (SSA), where islanded hybrid microgrids combining renewable generation, battery storage, and diesel backup offer a viable electrification pathway. This paper presents a multiobjective, multiperiod optimization framework for the design, sizing, and dispatch of such systems, with a case study for a remote community in Kenya. System sizing is optimized over a one-year horizon and dispatch over a representative day, both at hourly resolution. The formulation jointly minimizes lifecycle levelized cost of energy (LCOE), emissions, lost load, and dumped energy, while maximizing renewable penetration. Seven optimization algorithms are benchmarked; particle swarm optimization (PSO) achieves the best trade-off between runtime (63 s) and solution quality (normalized objective 0.146) and is used for subsequent analyses. The optimal configuration of solar PV, wind, lithium-ion battery storage, and diesel backup achieves a normalized LCOE of 0.46 USD per kWh with over 94 percent renewable penetration, outperforming alternatives. Pareto fronts highlight trade-offs between cost, emissions, and reliability, showing that cost-only optimization yields inferior outcomes. Sensitivity analyses identify fuel prices and discount rates as the most influential parameters in SSA contexts. A break-even distance analysis shows microgrids are economically competitive with grid extension at the study site. The dispatch model produces day-ahead schedules that are robust to short-term uncertainty, though extended wind lulls increase diesel reliance. This work fills a critical gap by providing a comprehensive multiobjective design and dispatch framework tailored to SSA resource, economic, and operational conditions.

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arXiv2026

Dynamic resource coordination can increase grid hosting capacity to support more renewables, storage, and electrified load growth

We show that dynamic coordination of distributed energy resources (DERs) can increase the capacity of low- and medium-voltage grids, improve reliability and power quality, and reduce solar curtailment. We develop three approaches to compute hosting capacity on a representative distribution grid with realistic scenarios. A deterministic iterative method provides insight into how dynamic operation and DER interactions enhance capacity and affect power flows, demonstrating clear gains over static methods even with low-to-moderate levels of storage and flexible demand. A stochastic programming approach jointly optimizes DER siting and sizing, showing that nodal colocation and complementary effects expand the feasible region of solar, heat pump, and battery penetrations by over 22X. This enables up to 200% solar, 100% battery, and 90% heat pump penetration. Batteries emerge as the most critical technology, followed by heat pumps and electric vehicles. A Monte Carlo-based extension shows that uncertainty significantly impacts hosting capacity and grid metrics, with 46% higher volatility under dynamic operation.

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arXiv2025

A game-theoretic, market-based approach to extract flexibility from distributed energy resources

We propose a market designed using game theory to optimally utilize the flexibility of distributed energy resources (DERs) like solar, batteries, electric vehicles, and flexible loads. Market agents perform multiperiod optimization to determine their feasible flexibility limits for power injections while satisfying all constraints of their DERs. This is followed by a Stackelberg game between the market operator and agents. The market operator as the leader aims to regulate the aggregate power injection around a desired value by leveraging the flexibility of their agents, and computes optimal prices for both electricity and flexibility services. The agents follow by optimally bidding their desired flexible power injections in response to these prices. We show the existence and uniqueness of a Nash equilibrium among all the agents and a Stackelberg equilibrium between all agents and the operator. In addition to deriving analytical closed-form solutions, we provide simulation results for a small example system to illustrate our approach.

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arXiv2024

Improving accuracy and convergence of federated learning edge computing methods for generalized DER forecasting applications in power grid

This proposal aims to develop more accurate federated learning (FL) methods with faster convergence properties and lower communication requirements, specifically for forecasting distributed energy resources (DER) such as renewables, energy storage, and loads in modern, low-carbon power grids. This will be achieved by (i) leveraging recently developed extensions of FL such as hierarchical and iterative clustering to improve performance with non-IID data, (ii) experimenting with different types of FL global models well-suited to time-series data, and (iii) incorporating domain-specific knowledge from power systems to build more general FL frameworks and architectures that can be applied to diverse types of DERs beyond just load forecasting, and with heterogeneous clients.

cs.LG↗

arXiv2024

Enhanced physics-informed neural networks (PINNs) for high-order power grid dynamics

We develop improved physics-informed neural networks (PINNs) for high-order and high-dimensional power system models described by nonlinear ordinary differential equations. We propose some novel enhancements to improve PINN training and accuracy and also implement several other recently proposed ideas from the literature. We successfully apply these to study the transient dynamics of synchronous generators. We also make progress towards applying PINNs to advanced inverter models. Such enhanced PINNs can allow us to accelerate high-fidelity simulations needed to ensure a stable and reliable renewables-rich future grid.

cs.LG↗

arXiv2024

Federated Learning Forecasting for Strengthening Grid Reliability and Enabling Markets for Resilience

We propose a comprehensive approach to increase the reliability and resilience of future power grids rich in distributed energy resources. Our distributed scheme combines federated learning-based attack detection with a local electricity market-based attack mitigation method. We validate the scheme by applying it to a real-world distribution grid rich in solar PV. Simulation results demonstrate that the approach is feasible and can successfully mitigate the grid impacts of cyber-physical attacks.

cs.LG↗

arXiv2024

Enhancing power grid resilience to cyber-physical attacks using distributed retail electricity markets

We propose using a hierarchical retail market structure to alert and dispatch resources to mitigate cyber-physical attacks on a distribution grid. We simulate attacks where a number of generation nodes in a distribution grid are attacked. We show that the market is able to successfully meet the shortfall between demand and supply by utilizing the flexibility of remaining resources while minimizing any extra power that needs to be imported from the main transmission grid. This includes utilizing upward flexibility or reserves of remaining online generators and some curtailment or shifting of flexible loads, which results in higher costs. Using price signals and market-based coordination, the grid operator can achieve its objectives without direct control over distributed energy resources and is able to accurately compensate prosumers for the grid support they provide.

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arXiv2023

Optimal transmission switching and grid reconfiguration for transmission systems via convex relaxations

In this paper, we formulate optimization problems to perform optimal transmission switching (OTS) in order to operate power transmission grids most efficiently. In any given electrical network, several of the transmission lines are generally equipped with switches, circuit breakers, and/or reclosers. The conventional practice is to operate the grid using a static or fixed configuration. However, it may be beneficial to dynamically reconfigure the grid through switching actions in order to respond to real-time demand and supply conditions. This has the potential to help reduce costs and improve efficiency. Furthermore, such OTS may be more crucial in future power grids with much higher penetrations of renewable energy sources, which introduce more variability and intermittency in generation. Similarly, OTS can potentially help mitigate the effects of unpredictable demand fluctuations (e.g. due to extreme weather). We explored and compared several different formulations for the OTS problems in terms of computational performance and optimality. I also applied them to small transmission test case networks as a proof of concept to see what the effects of applying OTS are.

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arXiv2023

Local retail electricity markets for distribution grid services

We propose a hierarchical local electricity market (LEM) at the primary and secondary feeder levels in a distribution grid, to optimally coordinate and schedule distributed energy resources (DER) and provide valuable grid services like voltage control. At the primary level, we use a current injection-based model that is valid for both radial and meshed, balanced and unbalanced, multi-phase systems. The primary and secondary markets leverage the flexibility offered by DERs to optimize grid operation and maximize social welfare. Numerical simulations on an IEEE-123 bus modified to include DERs, show that the LEM successfully achieves voltage control and reduces overall network costs, while also allowing us to decompose the price and value associated with different grid services so as to accurately compensate DERs.

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arXiv2022

Human Behavioral Models Using Utility Theory and Prospect Theory

Several examples of Cyber-physical human systems (CPHS) include real-time decisions from humans as a necessary building block for the successful performance of the overall system. Many of these decision-making problems necessitate an appropriate model of human behavior. Tools from Utility Theory have been used successfully in several problems in transportation for resource allocation and balance of supply and demand \citep{ben1985discrete}. More recently, Prospect Theory has been demonstrated as a useful tool in behavioral economics and cognitive psychology for deriving human behavioral models that characterize their subjective decision-making in the presence of stochastic uncertainties and risks, as an alternative to conventional Utility Theory \citep{kahneman_prospect_2012}. These models will be described in this article. Theoretical implications of Prospect Theory are also discussed. Examples will be drawn from transportation use cases such as shared mobility to illustrate these models as well as the distinctions between Utility Theory and Prospect Theory.

econ.GN↗

arXiv2022

Impacts of Dynamic Line Ratings on the ERCOT Transmission System

Grid regulators and participants are paying increasing attention to Dynamic Line Ratings (DLR) as a new approach to address transmission system bottlenecks. In this paper, a thorough comparison of DLR, Ambient Adjusted Ratings (AAR), and the traditional Static Line Ratings (SLR) are conducted on a synthetic ERCOT grid. Estimates of DLR and AAR are calculated using an equation based on heat balance physics, along with high-resolution weather data of temperature and wind velocities. A constraint generation method for contingency screening is developed for solving security-constrained optimal power flow. Numerical results suggest that employing DLR could double the benefits compared to those of AAR relative to SLR, in terms of system costs, renewable curtailment, and emissions.

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arXiv2022

A Hierarchical Local Electricity Market for a DER-rich Grid Edge

With increasing penetration of DERs in the distribution system, it is critical to design market structures that enable smooth integration of DERs. A hierarchical local electricity market (LEM) structure is proposed in this paper with a secondary market (SM) at the lower level representing secondary feeders and a primary market (PM) at the upper level, representing primary feeders, in order to effectively use DERs to increase grid efficiency and resilience. The lower level SM enforces budget, power balance and flexibility constraints and accounts for costs related to consumers, such as their disutility, flexibility limits, and commitment reliability, while the upper level PM enforces power physics constraints such as power balance and capacity limits, and also minimizes line losses. The hierarchical LEM is extensively evaluated using a modified IEEE-123 bus with high DER penetration, with each primary feeder consisting of at least three secondary feeders. Data from a GridLAB-D model is used to emulate realistic power injections and load profiles over the course of 24 hours. The performance of the LEM is illustrated by delineating the family of power-injection profiles across the primary and secondary feeders as well as corresponding local electricity tariffs that vary across the distribution grid. Together, it represents an overall framework for a Distribution System Operator (DSO) who can provide the oversight for the entire LEM.

math.OC↗

arXiv2021

Sensitivity Analysis of Passenger Behavioral Model for Dynamic Pricing of Shared Mobility on Demand

This paper provides a framework to quantify the sensitivity associated with behavioral models based on Cumulative Prospect Theory (CPT). These are used to design dynamic pricing strategies aimed at maximizing performance metrics of the Shared Mobility On-Demand Service (SMoDS), as solutions to a constrained nonlinear optimization problem. We analyze the sensitivity of both the optimal tariff as well as the optimal objective function with respect to CPT model parameters. In addition to deriving analytical solutions under certain assumptions, more general numerical results are obtained via computational experiments and simulations to analyze the sensitivity. We find that the model is relatively robust for small to moderate parameter perturbations. Although some of the trends in sensitivity are fairly general, the exact nature of variations in many cases depends heavily on the specific travel scenarios and modes being considered. This is primarily due to the complex nonlinearities in the problem, as well as the significant heterogeneity in passenger preferences across different types of trips.

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