arXiv subjects

Ankit Singhal

Publications and source records attributed to Ankit Singhal.

17 recordsLinked to original sources

arXiv2026

Grid-Aware Peer-to-Peer Energy Trading: A Learning-Augmented Framework

Distribution networks are transitioning from passive to active systems due to the growing integration of distributed energy resources (DERs). Peer to Peer (P2P) energy trading has emerged as a viable framework that enables local energy exchange among participants, represented here as aggregated microgrids (MGs). Incorporating network constraints is essential to ensure that P2P transactions remain physically feasible and consistent with grid's operating limits. However, existing P2P frameworks still lack advanced predictive mechanisms that allow prosumers to anticipate network feasibility or the distribution system operator (DSO) response during trade formulation. This paper proposes a learning augmented P2P and DSO interface that predicts the DSOs response to the proposed P2P trades, allowing prosumers to self-assess and refine their trading decisions. A supervised transformer based regression model is trained to enable MGs to locally predict the DSOs response without sharing their proposed trades, thereby reducing transaction overhead, alleviating DSO burden, and preserving information privacy. The proposed framework is validated on the modified IEEE 33 bus distribution power system with interconnected microgrids. Case studies are presented to validate the effectiveness of the proposed model in terms of market efficiency, trade acceptance and computational burden.

eess.SY↗

arXiv2026

Stabilizing Transformer Training Through Consensus

Standard attention-based transformers are known to exhibit instability under learning rate overspecification during training, particularly at high learning rates. While various methods have been proposed to improve resilience to such overspecification by modifying the optimization procedure, fundamental architectural innovations to this end remain underexplored. In this work, we illustrate that the consensus mechanism, a drop-in replacement for attention, stabilizes transformer training across a wider effective range of learning rates. We formulate consensus as a graphical model and provide extensive empirical analysis demonstrating improved stability across learning rate sweeps on text, DNA, and protein modalities. We further propose a hybrid consensus-attention framework that preserves performance while improving stability. We provide theoretical analysis characterizing the properties of consensus.

cs.LG↗

arXiv2024

Robust VAR Capability Curve of DER with Uncertain Renewable Generation

Active distribution system with high penetration of inverter based distributed energy resources (DER) can be utilized for VAR-related ancillary services. To utilize the DER flexibility, transmission system operator (TSO) must be presented with the aggregated DER flexibility of distribution system. However, the uncertainty in renewable generation questions the credibility of aggregated capability curve in practice. In this paper, we incorporate the uncertainty into aggregation process to develop a robust capability curve while preserving the real physics (unbalance and lossy nature) of distribution system. Statistical inference method is employed to quantify uncertainty in solar generation and quantified uncertainty is integrated into a chance constrained optimal power flow (OPF). It provides the grid operator with the dispatchable aggregated reactive power capability. The resulting capability curve with the associated probability can be harnessed by the TSO for decision making for both planning and operation.

eess.SY↗

arXiv2023

Resilient Control of Networked Microgrids using Vertical Federated Reinforcement Learning: Designs and Real-Time Test-Bed Validations

Improving system-level resiliency of networked microgrids is an important aspect with increased population of inverter-based resources (IBRs). This paper (1) presents resilient control design in presence of adversarial cyber-events, and proposes a novel federated reinforcement learning (Fed-RL) approach to tackle (a) model complexities, unknown dynamical behaviors of IBR devices, (b) privacy issues regarding data sharing in multi-party-owned networked grids, and (2) transfers learned controls from simulation to hardware-in-the-loop test-bed, thereby bridging the gap between simulation and real world. With these multi-prong objectives, first, we formulate a reinforcement learning (RL) training setup generating episodic trajectories with adversaries (attack signal) injected at the primary controllers of the grid forming (GFM) inverters where RL agents (or controllers) are being trained to mitigate the injected attacks. For networked microgrids, the horizontal Fed-RL method involving distinct independent environments is not appropriate, leading us to develop vertical variant Federated Soft Actor-Critic (FedSAC) algorithm to grasp the interconnected dynamics of networked microgrid. Next, utilizing OpenAI Gym interface, we built a custom simulation set-up in GridLAB-D/HELICS co-simulation platform, named Resilient RL Co-simulation (ResRLCoSIM), to train the RL agents with IEEE 123-bus benchmark test systems comprising 3 interconnected microgrids. Finally, the learned policies in simulation world are transferred to the real-time hardware-in-the-loop test-bed set-up developed using high-fidelity Hypersim platform. Experiments show that the simulator-trained RL controllers produce convincing results with the real-time test-bed set-up, validating the minimization of sim-to-real gap.

eess.SY↗

arXiv2022

Enhancing Cyber Resilience of Networked Microgrids using Vertical Federated Reinforcement Learning

This paper presents a novel federated reinforcement learning (Fed-RL) methodology to enhance the cyber resiliency of networked microgrids. We formulate a resilient reinforcement learning (RL) training setup which (a) generates episodic trajectories injecting adversarial actions at primary control reference signals of the grid forming (GFM) inverters and (b) trains the RL agents (or controllers) to alleviate the impact of the injected adversaries. To circumvent data-sharing issues and concerns for proprietary privacy in multi-party-owned networked grids, we bring in the aspects of federated machine learning and propose a novel Fed-RL algorithm to train the RL agents. To this end, the conventional horizontal Fed-RL approaches using decoupled independent environments fail to capture the coupled dynamics in a networked microgrid, which leads us to propose a multi-agent vertically federated variation of actor-critic algorithms, namely federated soft actor-critic (FedSAC) algorithm. We created a customized simulation setup encapsulating microgrid dynamics in the GridLAB-D/HELICS co-simulation platform compatible with the OpenAI Gym interface for training RL agents. Finally, the proposed methodology is validated with numerical examples of modified IEEE 123-bus benchmark test systems consisting of three coupled microgrids.

cs.LG↗

arXiv2022

Analyzing Distribution Transformer Degradation with Increased Power Electronic Loads

The influx of non-linear power electronic loads into the distribution network has the potential to disrupt the existing distribution transformer operations. They were not designed to mediate the excessive heating losses generated from the harmonics. To have a good understanding of current standing challenges, a knowledge of the generation and load mix as well as the current harmonic estimations are essential for designing transformers and evaluating their performance. In this paper, we investigate a mixture of essential power electronic loads for a household designed in PSCAD/EMTdc and their potential impacts on transformer eddy current losses and derating using harmonic analysis. The various scenarios have been studied with increasing PV penetrations. The peak load conditions are chosen for each scenario to perform a transformer derating analysis. Our findings reveal that in the presence of high power electronic loads (especially third harmonics), along with increasing PV generation may worsen transformer degradation. However, with a low amount of power electronic loads, additional PV generation helps to reduce the harmonic content in the current and improve transformer performance.

eess.SY↗

arXiv2022

Designing a Transactive Electric Vehicle Agent with Customer's Participation Preference

The proliferation of electric vehicles (EVs) and their inherent flexibility in charging timings make them an asset to improve grid performance. In contrast to direct control by a utility or autonomous price-based charging, the transactive control framework not only provides benefits to both grid and customers but also ensures customer autonomy. In this work, we design a transactive electric vehicle (TEV) agent that incorporates the EV owner's willingness to trade-off between savings and amenity in form of a slider, where the EV owner's amenity is characterized as vehicle readiness. Further, a privacy-preserving bidding formulation is proposed that also represents the customer's transactive preference. A transactive market mechanism is discussed that integrates the TEV Agents into the local retail market and reconciles with the current day-ahead and real-time market structure. It is demonstrated that the proposed slider is able to provide a preferred trade-off between savings and amenity to individual customers. At the same time, the market mechanism is shown to successfully reduce both peak prices and peak demand. A comparative investigation of V1G and V2G technologies with respect to the battery prices is also discussed.

eess.SY↗

arXiv2022

Optimization-Based Resiliency Verification in Microgrids via Maximal Adversarial Set Characterization

Critical energy infrastructures are increasingly relying on advanced sensing and control technologies for efficient and optimal utilization of flexible energy resources. Algorithmic procedures are needed to ensure that such systems are designed to be resilient to a wide range of cyber-physical adversarial events. This paper provides a robust optimization framework to quantify the range of adversarial perturbations that a system can accommodate without violating pre-specified resiliency metrics. An inner-approximation of the set of adversarial events which can be mitigated by the available flexibility is constructed using an optimization based approach. The proposed algorithm is illustrated on an islanded microgrid example: a modified IEEE 123-node feeder with distributed energy resources. Simulations are carried out to validate that the resiliency metrics are met for any event sampled from the constructed adversarial set for varying levels of available flexibility (energy reserves).

eess.SY↗

arXiv2021

Harmonic Modeling, Data Generation, and Analysis of Power Electronics-Interfaced Residential Loads

Integration of electronics-based residential appliances and distributed energy resources in homes is expected to rise with grid decarbonization. These devices may introduce significant harmonics into power networks that need to be closely studied in order to accurately model and forecast load. However, it can be difficult to obtain harmonic-rich voltage and current data -- necessary for identifying accurate load models -- for residential electrical loads. Recognizing this need, first a set of electronics-based end-use loads is identified and modeled in an electromagnetic transients program tool for a residence. Second, an impedance-varying method is proposed to generate harmonic data that captures harmonic propagation to the supply voltage and harmonic interactions among end-use loads connected to the same supply voltage. Third, a harmonic-rich dataset produced via the proposed methodology is demonstrated to successfully identify frequency coupling matrix-based harmonic load models using the least-squares method. Numerical results demonstrate the accuracy of the model. The impact of limited data availability on model identification is also explored.

eess.SP↗

arXiv2020

Coordinated Frequency and Voltage Regulation of Grid-Following and Grid-Forming Inverters

In a purely inverter-based microgrid, both grid-forming (GFM) and grid-following (GFL) inverters will have a crucial role to play in frequency/voltage regulation and maintaining power sharing through their grid support capabilities. Consequently, the coordination between these two technologies becomes increasingly important for optimal system performance. However, the existing work does not consider GFL's potential to participate in a secondary control in coordination with GFM, thus not able to utilize the full capability of inverter resources. In this paper, we show that it is possible to fully coordinate the GFL and GFM inverters to achieve accurate power sharing, frequency/voltage regulation, and circulating var mitigation in networked microgrids even without the support of any synchronous generators or the bulk power system. We use the leader-follower consensus framework to develop a GFM-GFL coordination control. The effectiveness of the proposed coordination is verified under different disturbances and communication degradation. In addition, we find that the proposed fully-coordinated secondary control outperforms other approaches such as un-coordinated and partially-coordinated secondary controls, in aspects of load sharing and frequency and voltage regulation. Overall, this study emphasizes the need and benefits of GFL-GFM coordination in microgrids.

eess.SY↗

arXiv2020

Predicting Hydroxyl Mediated Nucleophilic Degradation and Molecular Stability of RNA Sequences through the Application of Deep Learning Methods

Synthesis and efficient implementation mRNA strands has been shown to have wide utility, especially recently in the development of COVID vaccines. However, the intrinsic chemical stability of mRNA poses a challenge due to the presence of 2'-hydroxyl groups in ribose sugars. The -OH group in the backbone structure enables a base-catalyzed nucleophilic attack by the deprotonated hydroxyl on the adjacent phosphorous and consequent self-hydrolysis of the phosphodiester bond. As expected for in-line hydrolytic cleavage reactions, the chemical stability of mRNA strands is highly dependent on external environmental factors, e.g. pH, temperature, oxidizers, etc. Predicting this chemical instability using a computational model will reduce the number of sequences synthesized and tested through identifying the most promising candidates, aiding the development of mRNA related therapies. This paper proposes and evaluates three deep learning models (Long Short Term Memory, Gated Recurrent Unit, and Graph Convolutional Networks) as methods to predict the reactivity and risk of degradation of mRNA sequences. The Stanford Open Vaccine dataset of 6034 mRNA sequences was used in this study. The training set consisted of 3029 of these sequences (length of 107 nucleotide bases) while the testing dataset consisted of 3005 sequences (length of 130 nucleotide bases), in structured (Lowest Entropy Base Pair Probability Matrix) and unstructured (Nodes and Edges) forms. The stability of mRNA strands was accurately generated, with the Graph Convolutional Network being the best predictor of reactivity ($RMSE = 0.249$) while the Gated Recurrent Unit Network was the best at predicting risks of degradation ($RMSE = 0.266$). Combining all target variables, the GRU performed the best with 76% accuracy. Results suggest these models can be applied to understand and predict the chemical stability of mRNA in the near future.

q-bio.QM↗

arXiv2018

Deriving DERs VAR-Capability Curve at TSO-DSO Interface to Provide Grid Services

The multitudes of inverter-based distributed energy resources (DERs) can be envisioned as distributed reactive power (var) devices (\textit{mini-SVCs}) that can offer var flexibility at TSO-DSO interface. To facilitate this vision, a systematic methodology is proposed to derive an aggregated var capability curve of a distribution system with DERs at the substation level, analogous to a conventional bulk generator. Since such capability curve will be contingent to the operating conditions and network constraints, an optimal power flow (OPF) based approach is proposed that takes inverter headroom flexibility, unbalanced nature of system and coupling with grid side voltage into account along with changing operating conditions. Further, the influence of several factors such as compliance to IEEE 1547 on the capability curve is thoroughly investigated on an IEEE 37 bus and 123 bus distribution test system along with unbalanced DER proliferation. Validation with nonlinear analysis is presented along with demonstration of a scenario with T-D co-simulation.

math.OC↗

arXiv2018

Monitoring Long Term Voltage Instability due to Distribution & Transmission Interaction using Unbalanced {\mu}PMU & PMU Measurements

This paper extends the idea of the Thevenin equivalent into unbalanced 3{\phi} circuits and proposes a 3{\phi} long-term voltage stability indicator (VSI), that can identify critical loads in a system. Furthermore, in order to identify whether the voltage stability limit is due to the transmission network or a distribution network, a transmission-distribution distinguishing index (TDDI) is proposed. The novelty in the proposed indices is that they can account for the unbalance in the lines and loads, enabling them to use unbalanced phasor measurements naturally. This is supported by mathematical derivations and numerical results. A convex optimization formulation to estimate the 3{\phi} Thevenin equivalent using PMU & {\mu}PMU measurements is proposed, making it possible to calculate VSI and TDDI in an online model-free manner. Numerical simulations performed using co-simulation between Pypower and GridlabD are presented for the IEEE 9 bus and the 30 bus transmission networks combined with several modified IEEE 13 node and 37 node distribution networks. These case studies validate the proposed 3{\phi}-VSI and TDDI over a wide range of scenarios and demonstrate the importance of {\mu}PMU measurements in identifying the regions causing long term voltage instability.

math.OC↗

arXiv2018

Identifying Long Term Voltage Stability Caused by Distribution Systems vs Transmission Systems

Monitoring the long term voltage stability of the power grid is necessary to ensure its secure operation. This paper presents a new phasor based methodology that distinguishes between long term voltage stability caused by distribution systems versus transmission systems. From a conceptual understanding of a simplified system, a Transmission-Distribution Distinguishing Index (TDDI) is proposed to distinguish between the two scenarios. A methodology to calculate the TDDI for multi-bus systems using quasi-steady state phasor measurements is described and validating results are presented for the IEEE 9 Bus system with a load replaced by various distribution feeders. The results verify that the TDDI can indeed be used to distinguish between transmission limited and distribution limited systems. This information can be utilized by the operator to effectively choose controls in distribution and transmission systems to improve the system margin.

math.OC↗

arXiv2017

A Framework to Utilize DERs' VAR Resources to Support the Grid in an Integrated T-D System

Increasing penetration of inverter-based distributed energy resources (DERs) opens up interesting opportunities for the transmission systems. We present a hypothesis that the numerous DERs in var control mode can be seen as geographically distributed var devices (mini-SVCs) and if controlled properly, can be exploited to increase system flexibility by providing local var support to the grid as an ancillary service. Based on this premise, a var support framework is proposed in this paper. It utilizes a novel D-OPF formulation for unbalanced three-phase feeders enabling the estimation of the maximum var support that can be provided by the DERs to the grid at different operating points without compromising the distribution network performance. Further, a co-simulation method is developed to investigate the true impact of the proposed DER var support on the grid in an integrated Transmission-Distribution (T-D) system.

math.OC↗

arXiv2017

Long-Term Voltage Stability Assessment of an Integrated Transmission Distribution System

Long-term voltage stability assessment (VSA) of independent transmission (T-VSA) and distribution (D-VSA)systems have been studied since long to estimate load margins. However, their impacts on each other have been neglected due to simplified assumptions i.e. in transmission systems, loads are assumed to be aggregated, and in distribution systems, substation bus voltage is assumed to be constant. This work investigates the VSA of integrated transmission-distribution (TD-VSA) using PV curve superimposition approach and reveals the possibility that the overall system loadability may be limited by the distribution system rather than the transmission system. In this paper, we analyze why T-VSA and D-VSA are not reliable enough to estimate true load margin of the overall system. The analysis has been verified on an integrated test system in different scenarios with and without DER penetration. Overall, the paper points out the need for integrated analysis and further builds a case why it is essential to develop a realistic co-simulation framework for a reliable long-term VSA of large-scale coupled T-D systems.

math.AG↗

arXiv2017

Real-Time Local Volt/VAR Control Under External Disturbances with High PV Penetration

Volt/var control (VVC) of smart PV inverter is becoming one of the most popular solutions to address the voltage challenges associated with high PV penetration. This work focuses on the local droop VVC recommended by the grid integration standards IEEE1547, rule21 and addresses their major challenges i.e. appropriate parameters selection under changing conditions, and the control being vulnerable to instability (or voltage oscillations) and significant steady state error (SSE). This is achieved by proposing a two-layer local real-time adaptive VVC that has two major features i.e. a) it is able to ensure both low SSE and control stability simultaneously without compromising either, and b) it dynamically adapts its parameters to ensure good performance in a wide range of external disturbances such as sudden cloud cover, cloud intermittency, and substation voltage changes. A theoretical analysis and convergence proof of the proposed control is also discussed. The proposed control is implementation friendly as it fits well within the integration standard framework and depends only on the local bus information. The performance is compared with the existing droop VVC methods in several scenarios on a large unbalanced 3-phase feeder with detailed secondary side modeling.

math.OC↗