arXiv subjects

Md Umar Hashmi

Publications and source records attributed to Md Umar Hashmi.

At least 19 recordsLinked to original sources

arXiv2026

Fairness for distribution network operations and planning

The incorporation of fairness into the distribution network (DN) planning and operation has become a key goal of recent studies. The cost of implementing fairness, denominated the price of fairness (PoF), covers the efficiency that is renounced for attaining social cohesion through fair outcomes. Locational disparity makes fairness schemes emerge to level the consumers playing field. However, fairness encompasses a range of notions. From egalitarian to merit-based criteria, various metrics are implemented as a tool for measuring equitable utility distribution. These have different mathematical complexities, from linear to non-linear programming cases, which affect their overall applicability. Hence, this study compiles the overarching fairness notions and metrics, reviewing how these affect stakeholders and the inherent mathematical optimisation in resource allocation problems. The aim is to support consistent and transparent planning and decision-making within DN operations.

cs.AI↗

arXiv2026

Multi-Region Optimal Energy Storage Arbitrage

The increasing interconnection of power systems through AC and DC links enables energy storage units to access multiple electricity markets yet most existing arbitrage models remain limited to singlemarket participation This gap restricts understanding of the economic value and operational constraints associated with crossborder storage operation To address this an optimal multiregion energy storage arbitrage model is developed for a gridscale battery located at one end of an interconnector linking two distinct dayahead markets The formulation incorporates battery capacity and ramping limits converter and interconnector losses and marketspecific buying and selling prices Using disjunctive linearization of nonlinear terms this work exactly reformulates the multiregion energy arbitrage optimization as a mixedinteger linear programming problem The proposed formulation ensures that the battery either charges or discharges from all participating energy markets simultaneously at any given time Case studies using eight years of BelgianUK price data demonstrate that multiregion participation can increase arbitrage revenue by more than 40% compared to local energy arbitrage operation only while also highlighting the negative impact of interconnector congestion on achievable gains The results indicate that crossborder market access substantially enhances storage profitability while considering the cycle of battery and that the proposed formulation provides a computationally efficient framework for evaluating and operating storage assets in interconnected power systems Finally a pseudoefficiency term is introduced to improve battery utilization by discarding less profitable charging and discharging battery cycles

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arXiv2025

Uncertainty quantification in load profiles with rising EV and PV adoption: the case of residential, industrial, and office buildings

The integration of photovoltaic (PV) generation and electric vehicle (EV) charging introduces significant uncertainty in electricity consumption patterns, particularly at the distribution level. This paper presents a comparative study for selecting metrics for uncertainty quantification (UQ) for net load profiles of residential, industrial, and office buildings under increased DER penetration. A variety of statistical metrics is evaluated for their usefulness in quantifying uncertainty, including, but not limited to, standard deviation, entropy, ramps, and distance metrics. The proposed metrics are classified into baseline-free, with baseline and error-based. These UQ metrics are evaluated for increased penetration of EV and PV. The results highlight suitable metrics to quantify uncertainty per consumer type and demonstrate how net load uncertainty is affected by EV and PV adoption. Additionally, it is observed that joint consideration of EV and PV can reduce overall uncertainty due to compensatory effects of EV charging and PV generation due to temporal alignment during the day. Uncertainty reduction is observed across all datasets and is most pronounced for the office building dataset.

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arXiv2025

Grid-Aware Flexibility Operation of Behind-the-Meter Assets: A review of Objectives and Constraints

The high penetration of distributed energy resources (DERs) in low-voltage distribution networks (LVDNs) often leads to network instability and congestion. Discovering the flexibility potential of behind- the-meter (BTM) assets offers a promising solution to these challenges, providing benefits for both prosumers and grid operators. This review focuses on the objectives and constraints associated with the operation of BTM flexibility resources in LVDNs. We propose a new classification framework for network-aware flexibility modelling that incorporates prosumer objectives, flexibility sources, and both local and grid-level constraints. This review identifies research gaps in prosumer-centric grid considerations, control strategies, flexibility preferences, and scenarios in the use of BTM resources.

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arXiv2025

Fairness for distribution network hosting capacity

The integration of distributed generation (DG) is essential to the energy transition but poses challenges for lowvoltage (LV) distribution networks (DNs) with limited hosting capacity (HC). This study incorporates multiple fairness criteria, utilitarian, egalitarian, bounded, and bargaining, into the HC optimisation framework to assess their impact. When applied to LV feeders of different sizes and topologies, the analysis shows that bargaining and upper-bounded fairness provide the best balance between efficiency and fairness. Efficiency refers to maximising the social welfare of the LV DNs, while fairness is proportional to the minimisation of disparity in opportunity for installing DG. Feeder topology significantly influences fairness outcomes, while feeder size affects total HC and the inherent fairness of feeders. These results emphasise the importance of regulatory incentives and network designs in order to facilitate fair and efficient DG integration.

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arXiv2025

DER Hosting capacity for distribution networks: definitions, attributes, use-cases and challenges

The rapid adoption of distributed energy resources (DERs) has outpaced grid modernization, leading to capacity limitations that challenge their further integration. Hosting Capacity Assessment (HCA) is a critical tool for evaluating how much DER capacity a grid can handle without breaching operational limits. HCA serves multiple goals: enabling higher DER penetration, accelerating grid connection times, guiding infrastructure upgrades or flexible resource deployment, ensuring equitable policies, and improving grid flexibility while minimizing curtailment. HCA lacks a universal definition, varying by modelling approaches, uncertainty considerations, and objectives. This paper addresses five key questions to standardize and enhance HCA practices. First, it classifies HCA objectives associated with different stakeholders such as system operators, consumers, market operators and consumers. Second, it examines model attributes, including modelling sophistication, data requirements, and uncertainty handling, thus balancing complexity with computational efficiency. Third, it explores HCA applications, such as planning grid investments or operational decisions, and summarizes use cases associated with HCA. Fourth, it emphasizes the need for periodic updates to reflect dynamic grid conditions, evolving technologies, and new DER installations. Finally, it identifies challenges, such as ensuring data quality, managing computational demands, and aligning short-term and long-term goals. By addressing these aspects, this paper provides a structured approach to perform and apply HCA, offering insights for engineers, planners, and policymakers to manage DER integration effectively.

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arXiv2024

Distribution network reconfiguration for operational objectives: reducing voltage violation incidents and network losses

As the share of Distributed energy resources (DER) in the low voltage distribution network (DN) is expected to rise, a higher and more variable electric load and generation could stress the DNs, leading to increased congestion and power losses. To address these challenges, DSOs will have to invest in strengthening the network infrastructure in the coming decade. This paper looks to minimize the need for flexibility through dynamic DN reconfiguration. Typically, European DNs predominantly use manual switches. Hence, the network configuration is set for longer periods of time. Therefore, an opportunity is missed to benefit from more short-term dynamic switching. In this paper, a method is proposed which identifies the best manual switches to replace with remotely controlled switches based on their performance in terms of avoided voltage congestion incidents and DN power losses. The developed method is an exhaustive search algorithm which divides the problem into 3 subsequent parts, i.e. radial configuration identification, multi-period power flow and impact assessment for reconfigurable switch replacement on DN operation. A numerical evaluation shows that replacing the two top-ranked switches in the test case reduced the power losses by 4.51% and the voltage constraint violations by 38.17%. Thus, investing in only a few reconfigurable switches can substantially improve the operational efficiency of DNs.

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arXiv2024

Improved Physics-Informed Neural Network based AC Power Flow for Distribution Networks

Power flow analysis plays a critical role in the control and operation of power systems. The high computational burden of traditional solution methods led to a shift towards data-driven approaches, exploiting the availability of digital metering data. However, data-driven approaches, such as deep learning, have not yet won the trust of operators as they are agnostic to the underlying physical model and have poor performances in regimes with limited observability. To address these challenges, this paper proposes a new, physics-informed model. More specifically, a novel physics-informed loss function is developed that can be used to train (deep) neural networks aimed at power flow simulation. The loss function is not only based on the theoretical AC power flow equations that govern the problem but also incorporates real physical line losses, resulting in higher loss accuracy and increased learning potential. The proposed model is used to train a Graph Neural Network (GNN) and is evaluated on a small 3-bus test case both against another physics-informed GNN that does not incorporate physical losses and against a model-free technique. The validation results show that the proposed model outperforms the conventional physics-informed network on all used performance metrics. Even more interesting is that the model shows strong prediction capabilities when tested on scenarios outside the training sample set, something that is a substantial deficiency of model-free techniques.

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arXiv2024

Linear energy storage and flexibility model with ramp rate, ramping, deadline and capacity constraints

The power networks are evolving with increased active components such as energy storage and flexibility derived from loads such as electric vehicles, heat pumps, industrial processes, etc. Better models are needed to accurately represent these assets; otherwise, their true capabilities might be over or under-estimated. In this work, we propose a new energy storage and flexibility arbitrage model that accounts for both ramp (power) and capacity (energy) limits, while accurately modelling the ramp rate constraint. The proposed models are linear in structure and efficiently solved using off-the-shelf solvers as a linear programming problem. We also provide an online repository for wider application and benchmarking. Finally, numerical case studies are performed to quantify the sensitivity of ramp rate constraint on the operational goal of profit maximization for energy storage and flexibility. The results are encouraging for assets with a slow ramp rate limit. We observe that for resources with a ramp rate limit of 10% of the maximum ramp limit, the marginal value of performing energy arbitrage using such resources exceeds 65% and up to 90% of the maximum profit compared to the case with no ramp rate limitations.

eess.SY↗

arXiv2024

Analyzing electric vehicle, load and photovoltaic generation uncertainty using publicly available datasets

This paper aims to analyze three publicly available datasets for quantifying seasonal and annual uncertainty for efficient scenario creation. The datasets from Elaad, Elia and Fluvius are utilized to statistically analyze electric vehicle charging, normalized solar generation and low-voltage consumer load profiles, respectively. Frameworks for scenario generation are also provided for these datasets. The datasets for load profiles and solar generation analyzed are for the year 2022, thus embedding seasonal information. An online repository is created for the wider applicability of this work. Finally, the extreme load week(s) are identified and linked to the weather data measured at EnergyVille in Belgium.

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arXiv2024

Impact of Dynamic Operating Envelopes on Distribution Network Hosting Capacity for Electric Vehicles

The examination of the maximum number of electric vehicles (EVs) that can be integrated into the distribution network (DN) without causing any operational incidents has become increasingly crucial as EV penetration rises. This issue can be addressed by utilizing dynamic operating envelopes (DOEs), which are generated based on the grid status. While DOEs improve the hosting capacity of the DN for EVs (EV-HC) by restricting the operational parameters of the network, they also alter the amount of energy needed for charging each EV, resulting in a decrease in the quality of service (QoS). This study proposes a network-aware hosting capacity framework for EVs (EV-NAHC) that i) aims to assess the effects of DOEs on active distribution networks, ii) introduces a novel definition for HC and calculates the EV-NAHC based on the aggregated QoS of all customers. A small-scale Belgian feeder is utilized to examine the proposed framework. The results show a substantial increase in the EV-NAHC with low, medium, and high-daily charging energy scenarios.

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arXiv2023

Stochastic flexibility needs assessment: learnings from H2020 EUniversal's German demonstration

Operational flexibility needs assessment (FNA) is crucial for system operators to plan/procure flexible resources in order to avoid probable network issues. We implemented an FNA tool in the framework of the H2020 EUniversal project for the German demonstration. In this work, we summarize our learnings from the demo implementation to cope with the limited availability of measurement data. Using a reduced network model and key performance indicators, we evaluate the digital-twin results with real-world implementations. The paper aims to motivate future research directions by duly considering real-world limitations in their modelling and developing innovative tailor-made solutions for an improved decision support framework for system operators.

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arXiv2023

Multi-market Optimal Energy Storage Arbitrage with Capacity Blocking for Emergency Services

The future power system is increasingly interconnected via both AC and DC interconnectors. These interconnectors establish links between previously decoupled energy markets. In this paper, we propose an optimal multi-market energy storage arbitrage model that includes emergency service provisions for system operator(s). The model considers battery ramping and capacity constraints and utilizes operating envelopes calculated based on interconnector capacity, efficiency, dynamic energy injection and offshore wind generation in the day-ahead market. The arbitrage model considers two separate electricity prices for buying and selling of electricity in the two regions, connected via an interconnector. Using disjunctive linearization of nonlinear terms, we exactly reformulate the inter-regional energy arbitrage optimization as a mixed integer linear programming problem. We propose two capacity limit selection models for storage owners providing emergency services. The numerical analyses focus on two interconnections linking Belgium and the UK. The results are assessed based on revenue, operational cycles, payback period, shelf life and computation times.

math.OC↗

arXiv2023

Robust dynamic operating envelopes for flexibility operation using only local voltage measurement

With growing intermittency and uncertainty in distribution networks around the world, ensuring operational integrity is becoming challenging. Recent use cases of dynamic operating envelopes (DOEs) indicate that they can be utilized for network awareness for autonomous operation of flexibility, maximizing distributed generation integration, coordinating flexibility in different power networks and in resource planning. To this end, a novel framework is presented for generating decentralized DOEs in real-time using only the nodal voltage measurement and partially decentralized, risk-averse, robust DOEs in a time-ahead setting using voltage forecast scenarios. Chance constraint level is analytically implemented for avoiding extremely restrictive time-ahead DOEs with insufficient feasible regions for local energy optimization. Since the proposed DOE calculation framework uses none or limited centralized feedback, it is resilient to cyberattacks, communication failures, missing data and errors in network layout information. Numerical results showcase the DOE calculation framework in real-time using voltage magnitude measurements and in day-ahead timeframe using forecasted voltage scenarios. Furthermore, the DOEs are extended to form P-Q charts while considering power factor and converter capacity limits.

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arXiv2023

Perspectives on distribution network flexible and curtailable resource activation and needs assessment

{A curtailable and flexible resource activation framework for solving distribution network (DN) voltage and thermal congestions is used to quantify three important aspects with respect to modelling low voltage networks.} This framework utilizes the network states in the absence of such flexible or curtailable resources as the input for calculating flexibility activation signal (FAS). The FAS has some similarities with optimal power flow duals {associated with power balance constraint}. FAS due to drooping design, {incentivize corrective flexibility activation} prior to any network limit violations. {The nonlinear resource dispatch optimal power flow (RDOPF) utilizes FAS for the activation of flexible and curtailable resources. Solving the OPF problem for a large system is computationally intensive, and second-order cone (SOC) relaxation is often applied in the literature.} {First,} we highlight the multi-objective nature of SOC relaxed RDOPF. A Pareto front tuning mechanism {is proposed for choosing loss penalty factor} while reducing the optimality gap of the SOC relaxed RDOPF. {Secondly, we} present a methodology for evaluating temporal and locational flexibility needs assessment of a DN, which DSO's can utilize for flexibility planning {in operational timescales and procurement in the flexibility market}. {Lastly, we} quantify the impact of reactive power flexibility for a DN with varying load power factors. {Numerical simulations indicate that the presence of reactive flexibility reduces the active power flexibility needs by 50\% for the test feeder with 0.8 aggregated load power factor.}

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arXiv2023

Consensus based phase connectivity identification for distribution network with limited observability

The mitigation of distribution network (DN) unbalance and the use of single-phase flexibility for congestion mitigation requires accurate phase connection information, which is often not available. For a large DN, the naive phase identification proposed in the majority of the prior works using a single voltage reference does not scale well for a multi-feeder DN. We present a consensus algorithm-based phase identification mechanism which uses multiple three-phase reference points to improve the prediction of phases. Due to the absence of real measurements for a real-suburban German DN, the algorithms are developed and evaluated over synthetic data using a digital twin. To utilize strongly correlated measurements, the DN is clustered into zones. We observe those reference measurements located in the same zone as the single-phase consumer leads to accurate prediction of DN phases. Four consensus algorithms are developed and compared. Using numerical results, we recommend the most robust phase identification mechanism. In our evaluation, measurement error, and the impact of the neutral conductor are also assessed. We assume limited DN observability and apply our findings to a German DN without smart meters, but only less than 8% of nodes have measurement boxes along with single-phase consumers with a home energy management system. Voltage time series for 1 month (hourly sampled) is utilized. The numerical results indicate that for 1% accuracy class measurement, the phase connectivity of 308 out of 313 single-phase consumers in a German DN can be identified. Further, we also propose metrics quantifying the goodness of the phase identification. The phase identification framework based on consensus algorithms for DN zones is scalable for large DN and robust towards measurement errors as the estimation is not dependent on a single measurement point.

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arXiv2022

Can locational disparity of prosumer energy optimization due to inverter rules be limited?

To mitigate issues related to the growth of variable smart loads and distributed generation, distribution system operators (DSO) now make it binding for prosumers with inverters to operate under pre-set rules. In particular, the maximum active and reactive power set points for prosumers are based on local voltage measurements to ensure that inverter output does not cause voltage violations. However, such actions, as observed in this work, restrict the range available for local energy management, with more adverse losses on arbitrage profits for prosumers located farther away from the substation. The goal of the paper is three-fold: (a) to develop an optimal local energy optimization algorithm for activation of load flexibility and inverter-interfaced solar PV and energy storage under time-varying electricity prices; (b) to quantify the locational impact on prosumer arbitrage gains due to inverter injection rules prevalent in different energy markets; (c) to propose a computationally efficient hybrid inverter control policy which provides voltage regulation while substantially reducing locational disparity. Using numerical simulations on three identical prosumers located at different parts of a radial feeder, we show that our control policy is able to minimize locational disparity in arbitrage gains between customers at the beginning and end of the feeder to 1.4%, while PV curtailment is reduced by 91.7% compared to the base case with restrictive volt-Var and volt-watt policy.

eess.SY↗

arXiv2022

Optimal Operation of HVDC Interconnector: Irish Case

In September 2018 EirGrid launched the new electricity market. These new market arrangements integrate the all island electricity market with European electricity markets, making optimal use of cross border transmission assets. Ireland operates three operational HVDC interconnectors: Moyle, East-West, and Greenlink and one in development Celtic interconnector which connect Ireland to Scotland, Wales, and France respectively. Irish market operator, EirGrid, can maximize their operational profit by using the price difference in these electricity markets. We propose a profit maximization modelling which considers the line losses and price difference in these different electricity markets and identifies the optimal import/export of power using HVDC interconnectors. These models in future should incorporate the distribution losses, renewable energy curtailment, and Irish power network congestion levels. The proposed modeling is the first step towards implementing a multi-objective HVDC interconnector operating strategy.

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