arXiv subjects

Nanda Kishor Panda

Publications and source records attributed to Nanda Kishor Panda.

7 recordsLinked to original sources

arXiv2026

Risk-Based PV-Rich Distribution System Planning Using Generative AI

Hosting capacity (HC) assessment plays a critical role in distribution system planning under increasing penetration of distributed energy resources (DERs) and associated uncertainties in load and generation. However, conventional approaches often rely on deterministic worst-case evaluation, leading to overly conservative HC estimates. This paper introduces a risk-based framework for HC assessment that explicitly accounts for the frequency, intensity, and duration of voltage violations under uncertain operating conditions. A generative AI-based approach is employed to generate realistic, time-correlated load demand scenarios conditioned on projected energy consumption growth levels. These scenarios are then used to assess voltage violations and quantify their risk using probabilistic intensity, duration, and frequency (IDF) metrics. The results show that extreme-percentile (zero-risk) approaches significantly underestimate PV-HC by treating all violations equally, regardless of their likelihood or persistence. For instance, allowing a 5% risk level increases HC by approximately 18% for a 15 min violation duration. The proposed approach provides a practical tool for risk-informed distribution system planning under uncertainty.

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arXiv2025

GNN-DT: Graph Neural Network Enhanced Decision Transformer for Efficient Optimization in Dynamic Environments

Reinforcement Learning (RL) methods used for solving real-world optimization problems often involve dynamic state-action spaces, larger scale, and sparse rewards, leading to significant challenges in convergence, scalability, and efficient exploration of the solution space. This study introduces GNN-DT, a novel Decision Transformer (DT) architecture that integrates Graph Neural Network (GNN) embedders with a novel residual connection between input and output tokens crucial for handling dynamic environments. By learning from previously collected trajectories, GNN-DT tackles the sparse rewards limitations of online RL algorithms and delivers high-quality solutions in real-time. We evaluate GNN-DT on the complex electric vehicle (EV) charging optimization problem and prove that its performance is superior and requires significantly fewer training trajectories, thus improving sample efficiency compared to existing DT and offline RL baselines. Furthermore, GNN-DT exhibits robust generalization to unseen environments and larger action spaces, addressing a critical gap in prior offline and online RL approaches.

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arXiv2025

Quantifying the Aggregate Flexibility of Electric Vehicles Charging Stations for Dependable Congestion Management Products -- A Dutch Case Study

Electric vehicles (EVs) play a crucial role in the transition towards sustainable modes of transportation and thus are critical to the energy transition. As their number grows, managing the aggregate power of EV charging is crucial to maintain grid stability and mitigate congestion. This study analyses more than 500 thousand real charging transactions in the Netherlands to explore the challenge and opportunity for the energy system presented by increased charging needs and smart charging flexibility. Specifically, it quantifies the collective ability to provide dependable congestion management services according to the specifications of those services in the Netherlands. In this study, a data-driven model of charging behaviour is created to explore the implications of delivering dependable congestion management services at various aggregation levels and types of service. The probabilistic ability to offer different flexibility products, namely, redispatch and capacity limitation, for congestion management, is assessed for different categories of charging stations (CS) and dispatch strategies. These probabilities can help EV aggregators, such as charging point operators, make informed decisions about offering congestion mitigation products per relevant regulations and distribution system operators to assess their potential. Further, it is shown how machine learning models can be incorporated to predict the day-ahead consumption, followed by operationally predicting redispatch flexibility. The findings demonstrate that the timing of EV arrivals, departures, and connections plays a crucial role in determining the feasibility of product offerings, and dependable services can generally be delivered using a sufficiently large number of CSs.

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arXiv2025

Impact of Lead Time on Aggregate EV Flexibility for Congestion Management Services

Increased electrification of energy end-usage can lead to network congestion during periods of high consumption. Flexibility of loads, such as aggregate smart charging of Electric Vehicles (EVs), is increasingly leveraged to manage grid congestion through various market-based mechanisms. Under such an arrangement, this paper quantifies the effect of lead time on the aggregate flexibility of EV fleets. Simulations using real-world charging transactions spanning over different categories of charging stations are performed for two flexibility products (redispatch and capacity limitations) when offered along with different business-as-usual (BAU) schedules. Results show that the variation of tradable flexibility depends mainly on the BAU schedules, the duration of the requested flexibility, and its start time. Further, the implication of these flexibility products on the average energy costs and emissions is also studied for different cases. Simulations show that bidirectional (V2G) charging outperforms unidirectional smart charging in all cases.

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arXiv2024

Aggregate Peak EV Charging Demand: The Impact of Segmented Network Tariffs

Aggregate peak Electric Vehicle (EV) charging demand is a matter of growing concern for network operators as it severely limits the network's capacity, preventing its reliable operation. Various tariff schemes have been proposed to limit peak demand by incentivizing flexible asset users to shift their demand from peak periods. However, fewer studies quantify the effect of these tariff schemes on the aggregate level. In this paper, we compare the effect of a multi-level segmented network tariff with and without dynamic energy prices for individual EV users on the aggregate peak demand. Results based on real charging transactions from over 1200 public charging points in the Netherlands show that the segmented network tariff with flat energy prices results in more diverse load profiles with increasing aggregation, as compared to cost-optimized dispatch based on only dynamic day-ahead energy prices. When paired with dynamic energy prices, the segmented tariff still outperforms only dynamic energy price-based tariffs in reducing peaks. Results show that a balance between power thresholds and price per threshold is crucial in designing a suitable tariff, taking into account the needs of the power network. We also provide valuable insights to network operators by calculating the diversity factor for various peak demands per charging point.

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arXiv2024

Efficient Quantification and Representation of Aggregate Flexibility in Electric Vehicles

Aggregation is crucial to the effective use of flexibility, especially in the case of electric vehicles (EVs) because of their limited individual battery sizes and large aggregate impact. This research proposes a novel method to quantify and represent the aggregate charging flexibility of EV fleets within a fixed flexibility request window. These windows can be chosen based on relevant network operator needs, such as evening congestion periods. The proposed representation is independent of the number of assets but scales only with the number of discrete time steps in the chosen window. The representation involves $2T$ parameters, with T being the number of consecutive time steps in the window. The feasibility of aggregate power signals can be checked using $2T$ constraints and optimized using $2(2^T-1)$ constraints, both exactly capturing the flexibility region. Using a request window eliminates uncertainty related to EV arrival and departure times outside the window. We present the necessary theoretical framework for our proposed methods and outline steps for transitioning between representations. Additionally, we compare the computational efficiency of the proposed method with the common direct aggregation method, where individual EV constraints are concatenated.

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arXiv2023

Impact of Dynamic Tariffs for Smart EV Charging on LV Distribution Network Operation

With a growing share of electric vehicles (EVs) in our distribution grids, the need for smart charging becomes indispensable to minimise grid reinforcement. To circumvent the associated capacity limitations, this paper evaluates the effectiveness of different levels of network constraints and different dynamic tariffs, including a dynamic network tariff. A detailed optimisation model is first developed for public charging electric vehicles in a representative Dutch low voltage (LV) distribution network, susceptible to congestion and voltage problems by 2050 without smart charging of EVs. Later, a detailed reflection is made to assess the influence of the modelled features on the distribution system operator (DSO), charge point operator (CPO) costs, and the EVs' final state-of-charge (SOC) for both mono- (V1G) and bi-directional (V2G) charging. Results show that the dynamic network tariff outperforms other flat tariffs by increasing valley-filling. Consequently, compared to regular day-ahead pricing, a {significant} reduction in the frequency of congestion in the lines is achieved. In addition, V2G ensures the joint optimum for different stakeholders causing adequate EV user satisfaction, decreased CPO costs compared to conventional charging and fewer violations of grid constraints for the DSOs.

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