arXiv subjects

Petros Aristidou

Publications and source records attributed to Petros Aristidou.

At least 19 recordsLinked to original sources

arXiv2026

Dynamic Flexibility Requests in Local Flexibility Markets: Quantifying the DSO Willingness to Pay

Local Flexibility Markets (LFMs) require Distribution System Operators (DSOs) to determine both the quantity of flexibility to procure and the corresponding willingness to pay during market clearing. Existing approaches typically rely on unrealistic centralized AC-OPF clearing algorithms or strictly localized, static flexibility requests driven primarily by congestion management, while the economic value of flexibility is largely neglected. This paper proposes a dynamic flexibility-request methodology in which the DSO's willingness to pay is embedded directly into the market-clearing objective by monetizing transformer and cable aging, network losses, and voltage congestion. To enable computationally efficient clearing, exact convex piecewise-linear epigraph reformulations of the IEEE C57.91 transformer aging model and an Arrhenius-based cable aging model are developed and formally proven to preserve exactness. The proposed framework is validated on a modified CIGRE MV benchmark and compared with a recent state-of-the-art flexibility-request methodology. The results demonstrate significantly higher market liquidity, more efficient flexibility procurement, and improved network operations while preserving the market's transparency and non-discrimination principles.

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arXiv2026

A Benchmarking Case Study for Local Flexibility Markets: Network, Scenarios, and Open Inputs

External inputs to local flexibility markets, network models, demand scenarios, flexibility offers, and asset thermal, economic parameters do not change the clearing methodology, yet they are routinely defined under heterogeneous assumptions, which hinders reproducible benchmarking. This paper provides a complete, methodology-agnostic input set built around a modified CIGRE MV network with prescribed base-load and stress-load conditions, synthetic flexibility offers, wholesale prices, and transformer and cable parameters. Baseline AC operating points (voltages, loadings, and aging) are reported for both days so that alternative flexibility-request and clearing methods can be applied to the same inputs and compared fairly. The associated data are released openly on Zenodo.

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arXiv2026

Physics-Informed Neural Network Models for EMT Simulators

This is the first paper, to the best of our knowledge, to propose a framework that integrates Physics-Informed Neural Network (PINN) models in Electromagnetic Transient (EMT) simulations. Both industry and EMT simulation tools face emerging challenges, as the share of inverter-based resources in power grids rapidly grows. Vendors need to protect the Intellectual Property (IP) of their control design when sharing their component models with customers and system operators. EMT simulation tools need to increase computation speed while maintaining numerical stability. NN surrogates offer numerically stable models with higher degrees of IP protection, as they do not expose the underlying model structure and control parameters. We show that NN surrogates can accurately capture the dynamic behavior of both open-loop and closed-loop controllers, producing identical results in both a validated in-house EMT solver and commercial tools, such as PSCAD. Compared with the artificial delay methods used by commercial solvers, we show that NN surrogates are more numerically robust. Compared with iterative numerical solvers, we show that NN surrogates replacing components with algebraic loops (e.g., closed-loop controllers) increase computation speed by 40\% on average. We demonstrate our methods on PSCAD and the IEEE 39-bus system. Although the speed advantage vanishes when NN surrogates replace only a few components in large scale systems, NN-based models continue to offer enhanced IP protection and numerical stability. Our source code, in-house EMT solver, and the code for seamless integration to PSCAD are made publicly available.

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arXiv2026

Enhanced Optimal Power Flow Using a Trained Neural Network Surrogate for Distribution Grid Constraints

The growing penetration of distributed energy resources (DERs), electric vehicles (EVs), and heat pumps (HPs) in distribution networks underscores the need for secure, computationally efficient optimal power flow (OPF) solutions. Traditional OPF formulations often suffer from scalability limitations and may rely on relaxations/approximations whose exactness is not guaranteed. This paper proposes a framework in which a trained neural network (NN) surrogate is embedded directly within the OPF as a constraint replacement. Specifically, the nonlinear power-flow-to-voltage mapping is replaced by an exact mixed-integer linear encoding of the NN (i.e., the NN input-output map is represented without approximation), while all remaining OPF constraints are preserved. Using a realistic low-voltage network with integrated PV, EVs, and HPs, the proposed method achieves high voltage accuracy during post-solution AC power flow validation, with maximum deviations of less than 1.0 V in the examined test cases. The resulting NN-OPF problems are solved to global optimality within the MILP solver tolerance, and numerical results demonstrate substantially reduced computation time compared to nonlinear OPF models, with performance competitive with SOCP-based DistFlow formulations.

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arXiv2026

A Thermal-Electrical Co-Optimization Framework for Active Distribution Grids with Electric Vehicles and Heat Pumps

The growing electrification of transportation and heating through Electric Vehicles (EVs) and Heat Pumps (HPs) introduces both flexibility and complexity to Active Distribution Networks (ADNs). These resources provide substantial operational flexibility but also create tightly coupled thermal-electrical dynamics that challenge conventional network management. This paper proposes a unified co-optimization framework that integrates a calibrated 3R2C grey-box building thermal model into a network-constrained Optimal Power Flow (OPF). The framework jointly optimizes EVs, HPs, and photovoltaic systems while explicitly enforcing thermal comfort, Distributed Energy Resource (DER) limits, and full power flow physics. To maintain computational tractability, Second-Order Cone Programming (SOCP) relaxations are evaluated on a realistic low-voltage feeder. The analysis shows that, despite network heterogeneity violating some theoretical exactness conditions, the relaxation remains exact in practice. Comparative assessments of convex DistFlow, bus injection, and branch flow formulations reveal that convex DistFlow achieves sub-second runtimes and near-optimal performance even at high DER penetration levels. Simulations confirm the effectiveness of coordinated scheduling, yielding reductions of 41% in transformer aging, 54% in losses, and complete elimination of voltage violations, demonstrating the value of integrated thermal-electrical coordination in future smart grids.

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arXiv2023

Inertia-Aware Microgrid Investment Planning Using Tractable Decomposition Algorithms

The integration of the frequency dynamics into Micro-Grid (MG) investment and operational planning problems is vital in ensuring the security of the system in the post-contingency states. However, the task of including transient security constraints in MG planning problems is non-trivial. This is due to the highly non-linear and non-convex nature of the analytical closed form of the frequency metrics (e.g., frequency nadir) and power flow constraints. To handle this issue, this paper presents two algorithms for decomposing the MG investment planning problem into multiple levels to enhance computational tractability and optimality. Furthermore, the sensitivity of the decisions made at each level is captured by corresponding dual cutting planes to model feasible secure regions. This, in turn, ensures both the optimal determination and placement of inertia services and accelerates the convergence of the proposed decomposition algorithms. The efficient and effective performance of the proposed algorithms is tested and verified on an 18-bus Low Voltage (LV) network and a 30-bus Medium Voltage (MV) network under various operating scenarios.

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arXiv2022

Multiple Ancillary Services Provision by Distributed Energy Resources in Active Distribution Networks

The electric power system is currently experiencing radical changes stemming from the increasing share of renewable energy resources and the consequent decommissioning of conventional power plants based on synchronous generators. Since the principal providers of ancillary services are being phased out, new flexibility and reserve providers are needed. The proliferation of Distributed Energy Resources (DERs) in modern distribution networks has opened new possibilities for distribution system operators, enabling them to fill the market gap by harnessing the DER flexibility. This paper introduces a novel centralized MPC-based controller that enables the concurrent provision of voltage support, primary and secondary frequency control by adjusting the setpoints of a heterogeneous group of DERs in active distribution grids. The input-multirate control framework is used to accommodate the distinct timescales and provision requirements of each ancillary service and to ensure that the available resources are properly allocated. Furthermore, an efficient way for incorporating network constraints in the formulation is proposed, where network decomposition is applied to a linear power flow formulation together with network reduction. In addition, different timescale dynamics of the employed DERs and their capability curves are included. The performance of the proposed controller is evaluated on several case studies via dynamic simulations of the IEEE 33-bus system.

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arXiv2022

Using Quantile Forecasts for Dynamic Equivalents of Active Distribution Grids under Uncertainty

While distribution networks (DNs) turn from consumers to active and responsive intelligent DNs, the question of how to represent them in large-scale transmission network (TN) studies is still under investigation. The standard approach that uses aggregated models for the inverter-interfaced generation and conventional load models introduces significant errors to the dynamic modeling that can lead to instabilities. This paper presents a new approach based on quantile forecasting to represent the uncertainty originating in DNs at the TN level. First, we aquire a required rich dataset employing Monte Carlo simulations of a DN. Then, we use machine learning (ML) algorithms to not only predict the most probable response but also intervals of potential responses with predefined confidence. These quantile methods represent the variance in DN responses at the TN level. The results indicate excellent performance for most ML techniques. The tuned quantile equivalents predict accurate bands for the current at the TN/DN-interface, and tests with unseen TN conditions indicate robustness. A final assessment that compares the MC trajectories against the predicted intervals highlights the potential of the proposed method.

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arXiv2022

Improving Stability of Low-Inertia Systems using Virtual Induction Machine Synchronization for Grid-Following Converters

This paper presents a novel strategy for the synchronization of grid-following Voltage Source Converters (VSCs) in power systems with low rotational inertia. The proposed synchronization unit is based on emulating the physical properties of an induction machine and capitalizes on its inherent grid-friendly properties such as self-synchronization, oscillation damping, and standalone capabilities. To this end, the mathematical model of an induction machine is analyzed and reformulated to obtain the unknown grid frequency by processing the voltage and current measurements at the converter output. This eliminates the need for a Phase-Locked Loop (PLL) unit, traditionally employed in grid-following VSC control schemes, while simultaneously preserving the system- and device-level control. Furthermore, we provide the appropriate steps for obtaining an index-1 DAE representation of the induction-machine-based synchronization unit, suitable for stability analysis. Our analysis shows that replacing the PLL unit with the virtual induction machine-based synchronization may considerably improve the stability of systems with grid-following converters and facilitate the frequency containment. Furthermore, we validate the performance of the proposed synchronization unit through simulations and provide recommendations for its tuning.

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arXiv2022

Fast Mapping of Flexibility Regions at TSO-DSO Interfaces under Uncertainty

The gradual decommissioning of fossil fuel-driven power plants, that traditionally provide most operational flexibility in power systems, has led to more frequent grid stability issues. To compensate for the lack of flexible resources, Distributed Energy Resources (DERs) in distribution networks can be employed. To facilitate the use of DERs, the aggregated flexibility in a distribution grid is commonly represented on a $PQ$-plane displaying the feasible active and reactive power exchange with the upstream grid. This paper proposes a fast feasible operating region mapping mechanism that utilizes a linear power flow approximation in combination with linearized generator, current, and voltage constraints to construct a high-dimensional polyhedral feasible set of DER power injections. The obtained polytope is projected onto the $PQ$-plane using Fourier-Motzkin Elimination to represent the aggregate network flexibility. Additionally, uncertainty in DER generation is addressed using chance-constraints. Our analysis of a modified 33-bus IEEE test system demonstrates that the proposed method obtains more accurate approximations than former geometric methods and is ten times faster than the tested optimization-based method.

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arXiv2020

Towards a Sustainable Microgrid on Alderney Island Using a Python-based Energy Planning Tool

In remote or islanded communities, the use of microgrids (MGs) is necessary to ensure electrification and resilience of supply. However, even in small-scale systems, it is computationally and mathematically challenging to design low-cost, optimal, sustainable solutions taking into consideration all the uncertainties of load demands and power generations from renewable energy sources (RESs). This paper uses the open-source Python-based Energy Planning (PyEPLAN) tool, developed for the design of sustainable MGs in remote areas, on the Alderney island, the 3$^{rd}$ largest of the Channel Islands with a population of about 2000 people. A two-stage stochastic model is used to optimally invest in battery storage, solar power, and wind power units. Moreover, the AC power flow equations are modelled by a linearised version of the DistFlow model in PyEPLAN, where the investment variables are here-and-now decisions and not a function of uncertain parameters while the operation variables are wait-and-see decisions and a function of uncertain parameters. The $k$-means clustering technique is used to generate a set of best (risk-seeker), nominal (risk-neutral), and worst (risk-averse) scenarios capturing the uncertainty spectrum using the yearly historical patterns of load demands and solar/wind power generations. The proposed investment planning tool is a mixed-integer linear programming (MILP) model and is coded with Pyomo in PyEPLAN.

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arXiv2020

A Reinforcement Learning Approach for Fast Frequency Control in Low-Inertia Power Systems

The electric grid is undergoing a major transition from fossil fuel-based power generation to renewable energy sources, typically interfaced to the grid via power electronics. The future power systems are thus expected to face increased control complexity and challenges pertaining to frequency stability due to lower levels of inertia and damping. As a result, the frequency control and development of novel ancillary services is becoming imperative. This paper proposes a data-driven control scheme, based on Reinforcement Learning (RL), for grid-forming Voltage Source Converters (VSCs), with the goal of exploiting their fast response capabilities to provide fast frequency control to the system. A centralized RL-based controller collects generator frequencies and adjusts the VSC power output, in response to a disturbance, to prevent frequency threshold violations. The proposed control scheme is analyzed and its performance evaluated through detailed time-domain simulations of the IEEE 14-bus test system.

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arXiv2020

A Stochastic-Robust Approach for Resilient Microgrid Investment Planning Under Static and Transient Islanding Security Constraints

When planning the investment in Microgrids (MGs), usually static security constraints are included to ensure their resilience and ability to operate in islanded mode. However, unscheduled islanding events may trigger cascading disconnections of Distributed Energy Resources (DERs) inside the MG due to the transient response, leading to a partial or full loss of load. In this paper, a min-max-min, hybrid, stochastic-robust investment planning model is proposed to obtain a resilient MG considering both High-Impact-Low-Frequency (HILF) and Low-Impact-High-Frequency (LIHF) uncertainties. The HILF uncertainty pertains to the unscheduled islanding of the MG after a disastrous event, and the LIHF uncertainty relates to correlated loads and DER generation, characterized by a set of scenarios. The MG resilience under both types of uncertainty is ensured by incorporating static and transient islanding constraints into the proposed investment model. The inclusion of transient response constraints leads to a min-max-min problem with a non-linear dynamic frequency response model that cannot be solved directly by available optimization tools. Thus, in this paper, a three-stage solution approach is proposed to find the optimal investment plan. The performance of the proposed algorithm is tested on the CIGRE 18-node distribution network.

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arXiv2020

Active Distribution Grids Providing Voltage Support: The Swiss Case

The increasing installation of controllable Distributed Energy Resources (DERs) in Distribution Networks (DNs) opens up new opportunities for the provision of ancillary services to the Transmission Network (TN) level. As the penetration of smart meter devices and communication infrastructure in DNs increases, they become more observable and controllable with centralized optimization-based control schemes becoming efficient and practical. In this paper, we propose a centralized tractable Optimal Power Flow (OPF)-based control scheme that optimizes the real-time operation of active DNs, while also considering the provision of voltage support as an ancillary service to the TN. We embed in the form of various constraints the current voltage support requirements of Switzerland and investigate the potential benefit of ancillary services provision assuming different operational modes of the DER inverters. We demonstrate the performance of the proposed scheme using a combined HV-MV-LV test system.

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arXiv2020

Modelling of Variable-Speed Refrigeration for Fast-Frequency Control in Low-InertiaSystems

In modern power systems, shiftable loads contribute to the flexibility needed to increase robustness and ensure security. Thermal loads are among the most promising candidates for providing such service due to the large thermal storage time constants. This paper demonstrates the use of Variable-Speed Refrigeration (VSR) technology, based on brushless DC motors, for fast-frequency response. First, we derive a detailed dynamic model of a single-phase VSR unit suitable for time-domain and small-signal stability analysis in low-inertia systems. For analysing dynamic interactions with the grid, we consider the aggregated response of multiple devices. However, the high computational cost involved in analysing large-scale systems leads to the need for reduced-order models. Thus, a set of reduced-order models is derived though transfer function fitting of data obtained from time-domain simulations of the detailed model. The modelling requirements and the accuracy versus computational complexity trade-off are discussed. Finally, the time-domain performance and frequency-domain analyses reveal substantial equivalence between the full- and suitable reduced-order models, allowing the application of simplified models in large-scale system studies.

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arXiv2020

Economic Valuation and Pricing of Inertia in Inverter-Dominated Power Systems

This paper studies the procurement and pricing of inertial response using a frequency-constrained unit commitment formulation, which co-optimizes the provision of energy and inertia services while accounting for their complementary properties. The proposed approach builds on a two-step process that allows to differentiate between the units being online for energy purposes and the ones committed additionally solely for inertia provision. Subsequently, three novel pricing and payment schemes that reimburse inertia providers in a transparent and fair manner according to their individual participation are proposed. The analysis considers both synchronous and converter-based generators and provides insights regarding the impact of each pricing scheme on total system cost, as well as on the investment signals for technologies capable of offering affordable inertial response. The results show that all three methods will have a beneficial impact on frequency stability and aid the operator in ensuring system reliability, whereas the policy implications to different inertia providing units will vary between the payment schemes.

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arXiv2020

Improving Dynamic Performance of Low-Inertia Systems through Eigensensitivity Optimization

An increasing penetration of renewable generation has led to reduced levels of rotational inertia and damping in the system. The consequences are higher vulnerability to disturbances and deterioration of the dynamic response of the system. To overcome these challenges, novel converter control schemes that provide virtual inertia and damping have been introduced, which raises the question of optimal distribution of such devices throughout the network. This paper presents a framework for performance-based allocation of virtual inertia and damping to the converter-interfaced generators in a low-inertia system. This is achieved through an iterative, eigensensitivity-based optimization algorithm that determines the optimal controller gains. Two conceptually different problem formulations are presented and validated on a 3-area, 12-bus test system.

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arXiv2020

MPC-Based Fast Frequency Control of Voltage Source Converters in Low-Inertia Power Systems

A rapid deployment of renewable generation has led to significant reduction in the rotational system inertia and damping, thus making frequency control in power systems more challenging. This paper proposes a novel control scheme based on Model Predictive Control (MPC) for converter-interfaced generators operating in a grid-forming mode, with the goal of exploiting their fast response capabilities to provide fast frequency control service to the system. The controller manipulates converter power injections to limit the frequency nadir and rate-of-change-of-frequency after a disturbance. Both centralized and decentralized MPC approaches are considered and compared in terms of performance and practical implementation. Special attention is given to the decentralized controller by generating an explicit MPC solution to enhance computational efficiency and reduce hardware requirements. Simulation results obtained from a high-fidelity differential-algebraic equation model of the IEEE 39-bus system demonstrate the effectiveness of the proposed control schemes.

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