arXiv subjects

Harshal D. Kaushik

Publications and source records attributed to Harshal D. Kaushik.

10 recordsLinked to original sources

arXiv2026

Reinforcement Learning for Vehicle-to-Grid Voltage Regulation: Single-Hub to Multi-Hub Coordination with Battery-Aware Constraints

This paper presents a Vehicle-to-Grid (V2G) coordination framework using reinforcement learning (RL). {An intelligent control strategy based on the soft actor-critic algorithm is developed for voltage regulation through single and multi-hub charging systems while respecting realistic fleet constraints. A two-phase training approach integrates stability-focused learning with battery-aware deployment to ensure practical feasibility. Simulation studies on the IEEE 34-bus system validate the framework against a standard Volt-Var/Volt-Watt droop controller. Results indicate that the RL agent achieves performance comparable to the baseline control strategy in nominal scenarios. Under aggressive overloading, it provides robust voltage recovery (within 10% of the baseline) while prioritizing fleet availability and state-of-charge preservation, demonstrating the viability of constraint-aware learning for critical grid services.}

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arXiv2025

Multi-agent Power Grid Restoration Under Uncertainty Considering Coupled Transportation-Power Networks

Restoring power distribution systems after extreme events such as tornadoes presents significant logistical and computational challenges. The complexity arises from the need to coordinate multiple repair crews under uncertainty, manage interdependent infrastructure failures, and respect strict sequencing and routing constraints. Existing methods often rely on deterministic heuristics or simplified models that fail to capture the interdependencies between power and transportation networks, do not adequately model uncertainty, and lack representation of the interrelated dynamics and dependencies among different types of repair crews--leading to suboptimal restoration outcomes. To address these limitations, we develop a stochastic two-stage mixed-integer programming framework for proactive crew allocation, assignment, and routing in power grid restoration. The primary objective of our framework is to minimize service downtime and enhance power restoration by efficiently coordinating repair operations under uncertainty. Multiple repair crews are modeled as distinct agents, enabling decentralized coordination and efficient task allocation across the network. To validate our approach, we conduct a case study using the IEEE 8500-node test feeder integrated with a real transportation network from the Dallas-Fort Worth (DFW) region. Additionally, we use tornado event data from the DFW area to construct realistic failure scenarios involving damaged grid components and transportation links. Results from our case study demonstrate that the proposed method enables more coordinated and efficient restoration strategies. The model facilitates real-time disaster response by supporting timely and practical power grid restoration, with a strong emphasis on interoperability and crew schedule coordination.

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arXiv2025

Iterative Implicit Gradients for Nonconvex Optimization with Variational Inequality Constraints

We propose an optimization proxy in terms of iterative implicit gradient methods for solving constrained optimization problems with nonconvex loss functions. This framework can be applied to a broad range of machine learning settings, including meta-learning, hyperparameter optimization, large-scale complicated constrained optimization, and reinforcement learning. The proposed algorithm builds upon the iterative differentiation (ITD) approach. We extend existing convergence and rate analyses from the bilevel optimization literature to a constrained bilevel setting, motivated by learning under explicit constraints. Since solving bilevel problems using first-order methods requires evaluating the gradient of the inner-level optimal solution with respect to the outer variable (the implicit gradient), we develop an efficient computation strategy suitable for large-scale structures. Furthermore, we establish error bounds relative to the true gradients and provide non-asymptotic convergence rate guarantees.

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arXiv2025

Optimal Planning of Electric Vehicle Charging Stations: Integrating Public Charging Networks and Transportation Congestion

The adoption of electric vehicles (EVs) represents a critical shift in personal mobility, fueled by policy support and advancements in automotive technology. However, the expansion of EVs for long-distance travel is hindered by charging time concerns, the sparse distribution of charging stations, and the worsening waiting times due to congestion. The main objective of this work is two-fold: 1) first, to comprehensively analyze the existing public charging station robustness and effectively strategize for the new ones, and 2) secondly, to select the optimal chargers for long-distance journeys, by estimating the waiting time from current traffic congestion. This is achieved by accompanying effective EV charging strategies, pinpointing on the congestion points from the existing traffic, and the robustness of the current charging station infrastructure. Utilizing a real-time transportation and charging station dataset in Texas, we identify optimal charger placement strategies to minimize travel time by examining the congestion and charging time trade-offs. Our findings suggest that maximizing the constant current phase during charging enhances efficiency, crucial for long-distance travel. On the contrary, we also explore the negative impact of congestion on travel times and we conclude that sometimes it might be beneficial to exceed the constant current phase to avoid the congested charging stations.

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arXiv2025

Electric Vehicle Charger Infrastructure Planning: Demand Estimation, Coverage Optimization Over an Integrated Power Grid

For electrifying the transportation sector, deploying a strategically planned and efficient charging infrastructure is essential. This paper presents a two-phase approach for electric vehicle (EV) charger deployment that integrates spatial point-of-interest analysis and maximum coverage optimization over an integrated spatial power grid. Spatial-focused studies in the literature often overlook electrical grid constraints, while grid-focused work frequently considers statistically modeled EV charging demand. To address these gaps, a new framework is proposed that combines spatial network planning with electrical grid considerations. This study approaches EV charger planning from a perspective of the distribution grid, starting with an estimation of EV charging demand and the identification of optimal candidate locations. It ensures that the capacity limits of newly established chargers are maintained within the limits of the power grid. This framework is applied in a test case for the Dallas area, integrating the existing EV charger network with an 8500-bus distribution system for comprehensive planning.

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arXiv2024

Distribution Network Restoration: Resource Scheduling Considering Coupled Transportation-Power Networks

Optimal decision-making is key to efficient allocation and scheduling of repair resources (e.g., crews) to service affected nodes of large power grid networks. Traditional manual restoration methods are inadequate for modern smart grids sprawling across vast territories, compounded by the unpredictable nature of damage and disruptions in power and transportation networks. This paper develops a method that focuses on the restoration and repair efforts within power systems. We expand upon the methodology proposed in the literature and incorporate a real-world transportation network to enhance the realism and practicality of repair schedules. Our approach carefully devises a reduced network that combines vulnerable components from the distribution network with the real transportation network. Key contributions include dynamically addressing a coupled resource allocation and capacitated vehicle routing problem over a new reduced network model, constructed by integrating the power grid with the transportation network. This is performed using network heuristics and graph theory to prioritize securing critical grid segments. A case study is presented for the 8500 bus system.

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arXiv2022

An Incremental Gradient Method for Optimization Problems with Variational Inequality Constraints

We consider minimizing a sum of agent-specific nondifferentiable merely convex functions over the solution set of a variational inequality (VI) problem in that each agent is associated with a local monotone mapping. This problem finds an application in computation of the best equilibrium in nonlinear complementarity problems arising in transportation networks. We develop an iteratively regularized incremental gradient method where at each iteration, agents communicate over a cycle graph to update their solution iterates using their local information about the objective and the mapping. The proposed method is single-timescale in the sense that it does not involve any excessive hard-to-project computation per iteration. We derive non-asymptotic agent-wise convergence rates for the suboptimality of the global objective function and infeasibility of the VI constraints measured by a suitably defined dual gap function. The proposed method appears to be the first fully iterative scheme equipped with iteration complexity that can address distributed optimization problems with VI constraints over cycle graphs. Preliminary numerical experiments for a transportation network problem and a support vector machine model are presented.

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arXiv2021

Distributed Randomized Block Stochastic Gradient Tracking Method

We consider distributed optimization over networks where each agent is associated with a smooth and strongly convex local objective function. We assume that the agents only have access to unbiased estimators of the gradient of their objective functions. Motivated by big data applications, our goal lies in addressing this problem when the dimensionality of the solution space is possibly large and consequently, the computation of the local gradient mappings may become expensive. We develop a randomized block-coordinate variant of the recently developed distributed stochastic gradient tracking (DSGT) method. We derive non-asymptotic convergence rates of the order $1/k$ and $1/k^2$ in terms of an optimality metric and a consensus violation metric, respectively. Importantly, while block-coordinate schemes have been studied for distributed optimization problems before, the proposed algorithm appears to be the first randomized block-coordinate gradient tracking method that is equipped with the aforementioned convergence rate statements. We validate the performance of the proposed method on the MNIST and a synthetic data set under different network settings.

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arXiv2021

An Incremental Gradient Method for Large-scale Distributed Nonlinearly Constrained Optimization

Motivated by applications arising from sensor networks and machine learning, we consider the problem of minimizing a finite sum of nondifferentiable convex functions where each component function is associated with an agent and a hard-to-project constraint set. Among well-known avenues to address finite sum problems is the class of incremental gradient (IG) methods where a single component function is selected at each iteration in a cyclic or randomized manner. When the problem is constrained, the existing IG schemes (including projected IG, proximal IAG, and SAGA) require a projection step onto the feasible set at each iteration. Consequently, the performance of these schemes is afflicted with costly projections when the problem includes: (1) nonlinear constraints, or (2) a large number of linear constraints. Our focus in this paper lies in addressing both of these challenges. We develop an algorithm called averaged iteratively regularized incremental gradient (aIR-IG) that does not involve any hard-to-project computation. Under mild assumptions, we derive non-asymptotic rates of convergence for both suboptimality and infeasibility metrics. Numerically, we show that the proposed scheme outperforms the standard projected IG methods on distributed soft-margin support vector machine problems.

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arXiv2021

A Method with Convergence Rates for Optimization Problems with Variational Inequality Constraints

We consider a class of optimization problems with Cartesian variational inequality (CVI) constraints, where the objective function is convex and the CVI is associated with a monotone mapping and a convex Cartesian product set. This mathematical formulation captures a wide range of optimization problems including those complicated by the presence of equilibrium constraints, complementarity constraints, or an inner-level large scale optimization problem. In particular, an important motivating application arises from the notion of efficiency estimation of equilibria in multi-agent networks, e.g., communication networks and power systems. In the literature, the iteration complexity of the existing solution methods for optimization problems with CVI constraints appears to be unknown. To address this shortcoming, we develop a first-order method called averaging randomized block iteratively regularized gradient (aRB-IRG). The main contributions include: (i) In the case where the associated set of the CVI is bounded and the objective function is nondifferentiable and convex, we derive new non-asymptotic suboptimality and infeasibility convergence rate statements in a mean sense. We also obtain deterministic variants of the convergence rates when we suppress the randomized block-coordinate scheme. Importantly, this paper appears to be the first work to provide these rate guarantees for this class of problems. (ii) In the case where the CVI set is unbounded and the objective function is smooth and strongly convex, utilizing the properties of the Tikhonov trajectory, we establish the global convergence of aRB-IRG in an almost sure and a mean sense. We provide the numerical experiments for computing the best Nash equilibrium in a networked Cournot competition model.

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