State estimation is a cornerstone of power system control-center operations, and its robust operation is increasingly a cyber-physical security concern as modern grids become more digitalized and communication-intensive. Neural network-based approaches have gained attention as alternatives to conventional model-based state estimation methods. Physics-Informed Neural Networks (PINNs), which embed power-flow consistency into the learning objective, have shown improved accuracy over existing approaches. This work proposes a PINN-based model for Power System State Estimation (PSSE) that protects the estimation process against the stealth-constrained AC False Data Injection Attacks (FDIAs) considered in this study. The model is developed without adversarial training. Instead, a dynamic loss-weighting formulation based on homoscedastic uncertainty learns the relative scaling of supervised data-fit and physics-residual terms during training, reducing sensitivity to manual weight tuning. Robustness is evaluated on the IEEE 118-bus system using representative stealthy-FDIA families including state distortion, load redistribution, line overloading, and residual-constrained stealth corruption. Performance is measured using Mean Absolute Error (MAE) on voltage magnitudes and phase angles. Results demonstrate higher accuracy and stability than existing fixed-weight PINN variants.

The massive deployment of Distributed Energy Resources (DERs) on distribution grids presents both challenges and opportunities for system operators. Distribution System Operators (DSOs) must actively manage their network to ensure security constraints and should collaborate effectively with the Transmission System Operators (TSOs) to enhance the overall power system reliability and efficiency. The topic of TSO-DSO coordination has drawn a lot of attention in recent years and several coordination models have been integrated into the balancing market framework. Nevertheless, depending on the coordination model, which defines roles and responsibilities among market participants, there exists a risk of violating grid operational constraints when DERs are allowed to participate in transmission grid-level markets. Here, we adopt a decentralized coordination model from existing literature, that enables active distribution grid management. This work builds upon this coordination model and proposes a novel DERs market order prequalification scheme that enables the participation of DERs in transmission-level balancing markets while ensuring zero distribution grid congestion. The proposed scheme generates a feasible region that comprises the original DERs market orders. To demonstrate its efficacy and compare its performance against existing methods, we apply the proposed scheme to a power system consisting of both transmission and distribution levels, utilizing the IEEE 9-bus test system and a 463-bus medium voltage distribution system, respectively.

The growing digitalization of power transmission systems has introduced vulnerabilities to cyberattacks, particularly sensor manipulation, which can compromise control center monitoring and control applications. Conventional monitoring methods, including traditional state estimation and data-driven machine learning approaches, may face challenges in maintaining accuracy and reliability when confronted with such attacks. This paper proposes a physics-informed neural network (PINN)-based state estimation model to enhance robustness. By embedding power system physics, the PINN approach leverages both data-driven insights and physical constraints, improving robustness and accuracy in case of cyberattacks. The model’s performance is evaluated under diverse attack scenarios, comparing it to an equivalently sophisticated purely data-driven machine learning model. The results show that the PINN-based approach significantly improves robustness and accuracy, with or without the presence of attacks, offering a promising solution for robustifying power grid state estimation.

The rapid decentralization of power systems has driven a paradigm shift, with Distribution System Operators (DSOs) actively managing distribution grids to address challenges posed by the high penetration of Distributed Energy Resources (DERs). Activation of DERs in external electricity markets by third-party actors can potentially violate distribution grid operational constraints. To prevent this, it is essential to incorporate the grid’s feasible region, representing its operational limits defined by the AC power flow model, into market clearing processes. Nonetheless, despite being exact, this model generates a nonlinear and non-convex feasible region for the market, making the problem challenging to solve and unsuitable for near real-time applications. Toward this direction, this work investigates the applicability of two linear, approximate power flow models designed to overcome the non-convexities of the exact model in near real-time market operations. Specifically, the performance of the approximate models is compared to the exact model in terms of aggregate flexibility, solution feasibility, and computational complexity. Simulation results from a case study using three different test feeders show that the approximate models have significantly lower computational complexity. However, their performance in aggregate flexibility and solution feasibility is highly influenced by the grid’s size and operating state.

To facilitate the energy transition towards a decarbonized energy sector, distribution system operators will have to address several challenges by upgrading and redesigning the operation of the distribution grid. This review paper presents an overview of the operational challenges of low voltage distribution grids (LVDGs) operating under a high penetration of photovoltaic systems (PVs) and electric vehicles (EVs). Specifically, the impacts of PVs and EVs on the voltage profile, net-demand profile and network congestion, as well as network asymmetry are demonstrated and discussed. This is achieved by utilizing real measurements from LVDGs in the Cyprus power system and by examining different case studies that consider future operating conditions. A detailed review of management solutions available in the literature for active LVDGs is then presented that aim to mitigate the detrimental impacts of PVs and EVs. These include wired solutions such as on-line-tap changing transformers, reactive power control by smart inverters, active power control through PV curtailments, energy storage systems, EV charging strategies, and phase balancing schemes. In addition, several management solutions are implemented to representative LV feeders, which have been made publicly available, where their effectiveness and potential areas of improvement are evaluated.

With the increasing penetration of HVDC technologies in today's power systems, especially for interconnecting neighboring grids—including those that are renewable-rich—there is a need to explore their contribution to system reliability and resilience in the context of cascading failures. This paper introduces a holistic framework for quantifying and mitigating cascading risks in HVDC-interconnected systems. It considers extreme events in addition to credible or expected events that threaten the resilience of interconnected systems, as evidenced by recent cascading blackouts with cross-border propagation impacts in Europe and worldwide. To achieve this, advanced dynamic cascading failure modelling is leveraged to simulate and quantify the cascading effects in HVDC-interconnected systems, particularly focusing on frequency stability and large-scale disturbances. This sheds light on the influence of HVDC on mitigating the propagation of non-local cascading events with cross-border impacts in interconnected systems, attributed to its “firewall” property. It also seamlessly integrates the dynamic cascading simulator with operational strategies, specifically controlled islanding, to further mitigate both local and non-local cascading impacts, especially addressing cross-border propagation, in interconnected systems. The simulation results on HVDC-interconnected test systems demonstrate the efficiency of the proposed work in significantly reducing cascade metrics, including Expected Demand-Not-Served (EDNS) and, notably, Conditional Value-at-Risk (CVaR) which captures tail risk events.

The increasing penetration of renewable energy sources (RES) into power systems challenges the system operators for the safe and reliable operation of the system. In isolated power systems with a high share of RES, operators are often forced to apply RES curtailments to satisfy the operational constraints. These curtailments result in negative socioeconomic impacts due to the loss of clean energy. This work investigates the role of battery energy storage systems (BESSs) in reducing RES curtailments, CO2 emissions, and operating cost in isolated power systems. Towards this direction, a long-term unit commitment (UC) model that enables the performance of annual power system studies is used and modified to incorporate large-scale BESSs. The long-term UC model is applied to the isolated power system of Cyprus using real data. Simulation results indicate a decrease in annual RES curtailments from 16.9% to 1.6% as the BESS capacity increases from 0 to 2500 MWh, highlighting the necessity of deploying energy storage systems to enable an increased and effective RES penetration in isolated grids.

Artificial Neural Networks (ANNs) have proven to be powerful computational tools for various industrial processes, including numerous applications in power systems. This paper employs ANNs to determine the location of a single line-to-ground fault within a transmission line. Two different ANN-based approaches are proposed and examined to determine the most accurate for fault localization. To generate realistic training data for the two ANN-based approaches, fault simulations are performed using DIgSILENT software in the IEEE 9-bus system, considering various fault locations, fault resistances, and measurement noise levels. The performance of the trained ANN model is validated using synthesized data that the ANN model has never experienced considering different fault locations and fault resistances.

This paper investigates the impact of the volt/var control strategy on the lifespan of grid-connected photovoltaic (PV) inverters. With the increasing penetration of PV systems in modern distribution networks, volt/var control has become a cornerstone of grid codes to enhance voltage regulation and grid stability. This strategy enables PV inverters to inject or absorb reactive power dynamically according to the voltage conditions sensed by each inverter at the point of common coupling to mitigate intense voltage fluctuation. However, while volt/var control improves grid performance, it imposes additional operational demands on PV inverters, affecting their thermal performance, reliability, and lifespan. The study highlights that the degradation of PV inverters is significant under volt/var control and unfairly distributed, depending on the location of the installed inverter in the distribution network. For investigating the lifetime of PV inverters, a comprehensive long-term lifetime model is developed, incorporating thermal cycling and electrical stress caused by real and reactive power. The model quantifies the cumulative degradation of key inverter components under volt/var support, which is highly affect by the installation location of each PV inverter. The findings underline the need for modifications to conventional volt/var strategies to ensure efficient voltage control and fair degradation across inverters.

Synchronized Measurement Technology has significantly advanced real-time monitoring and control applications for power system control centers. A key component, the Phasor Measurement Unit (PMU), is now widely deployed in transmission systems, enhancing system monitoring and control. PMU-rich systems also enable the refinement of critical modeling parameters, such as transmission line sequence parameters, used in control center applications and substation protection relays. This paper discusses the estimation of zero-sequence parameters of transmission lines, essential for fault analysis in control centers and distance protection relays. Two key factors impacting estimation accuracy are examined, namely the PMU measurement and fault location uncertainties. Their effects on zero sequence line parameter estimation accuracy and consequently on the performance of distance relays are analyzed in the IEEE 9-bus system.

Modern power systems face significant challenges in state estimation and real-time monitoring, particularly regarding response speed and accuracy under faulty conditions or cyber-attacks. This article proposes a hybrid approach using physics-informed neural networks (PINNs) to enhance the accuracy and robustness of power system state estimation. By embedding physical laws into the neural network architecture, PINNs improve estimation accuracy for transmission grid applications under both normal and faulty conditions, while also showing potential in addressing security concerns, such as data manipulation attacks. Experimental results show that the proposed approach outperforms traditional machine learning models, achieving up to $\sim$83% higher accuracy on unseen subsets of the training dataset and $\sim$65% better performance on entirely new, unrelated datasets. Experiments also show that during a data manipulation attack against a critical bus in a system, the PINN can be up to $\sim$93% more accurate than an equivalent neural network.

Harmonic distortions have adverse effects on the operation of the power system. Harmonic sources in low voltage distribution grids (LVDG) are highly dispersed in nature. Therefore, managing the resulting voltage and current distortions through conventional measures, such as passive or active power filters, becomes challenging. This work coordinates the harmonic injection capabilities of enhanced photovoltaic inverters to form a distributed active power filter. The proposed method aims to reduce the voltage and current distortions of the LVDG with fair utilization of inverters. Towards this direction, a lexicographic optimization scheme is formulated to derive the inverters’ harmonic current set-points by (i) minimizing the harmonic voltage limit violations, (ii) maximizing the inverter utilization fairness based on Jain’s fairness index, and (iii) minimizing the compensating currents of the inverters to reduce their lifetime degradation. The resulting optimization model is non-convex, thus a convex second-order cone program (SOCP) is formulated by relaxing the non-convex constraints and reformulating the Jain’s index as an SOCP constraint. Moreover, an iterative algorithm is developed to find the feasible operating point that maximizes the Jain’s fairness value. Simulation results indicate the superiority of the proposed scheme compared to existing methodologies in mitigating harmonic distortions in three-phase four-wire LVDGs.

This paper outlines the testing plan and the field measurement results employed in the development and implementation of Smart5Grid’s UC#4 – Real-Time Wide Area Monitoring (WAM) of interconnected systems. As the integration of 5G infrastructure gains prominence in the Information and Communication Technology (ICT) domain, Smart5Grid leverages the capabilities of 5G Networks to enhance smart grid functionalities. The testing plan encompasses rigorous assessment procedures to evaluate the network performance in terms of Delay, Reliability and Availability of Smart5Grid in diverse operational scenarios between the two neighbouring countries of Greece and Bulgaria. Through detailed field measurements gathered by the Phasor Measurement Units (PMUs), this study aims to validate the system's capability in seamlessly integrating distributed energy resources, optimizing grid performance, ensuring reliability and facilitating dynamic response to evolving demands and conditions. The findings from these field measurements provide valuable insights into the effectiveness and viability of integrating 5G technologies into Smart5Grid, contributing to advancements in the development of next-generation smart grid solutions.

The increasing penetration of photovoltaic systems (PV) in distribution grids can create significant over-voltage conditions due to intense reverse power flow. In addition to reactive power control, PV curtailments are often employed for voltage regulation to ensure compliance with the regulatory limits. However, reducing the output power of a PV system results to a revenue drop for the owners, raising concerns about fairness. Relevant works often address this issue through penalty terms in the objective function, but most works neglect the trade-off between fairness and total curtailments, i.e., increasing fairness leads to an increase of total curtailments. This paper proposes a novel methodology to enhance the fairness of PV curtailments for voltage regulation while ensuring that the increase in total curtailments is within a specified acceptable limit. In this direction, the Jain’s fairness index is used to quantitatively measure fairness, which is incorporated in the optimization process as a second order cone program constraint. An iterative algorithm based on the bisection method is then developed to increase fairness until the allowable total curtailments are reached. Simulation results demonstrate the capability of the proposed method to improve the fairness of PV curtailments, considering different levels of allowable total curtailments.

Reducing the energy consumption of buildings is vital for achieving the European Union's climate targets. Towards this direction, one important strategy is to intelligently manage the indoor temperature of buildings to minimize energy con-sumption without compromising the comfort levels of residents. This can be accomplished through model predictive control frameworks that utilize low-complexity thermal dynamic models tailored to the building and heating/cooling system character-istics. This work builds on a single-zone first-order resistance-capacitance (RC) model to develop a multi-zone 1R1C model for the prediction of indoor temperatures in multi-zone buildings. To enhance the prediction accuracy of the derived model, a bimodal multi-zone 1R1C model is further proposed that handles separately the on and off states of the heating system. An estimation framework is then developed based on the least square method to estimate the parameters of the two models using datasets obtained from the EnergyPlus simulator and historical weather data. Simulation results showcase the capability of the proposed models to accurately predict the temperatures of the different zones. The results also demonstrate the superiority of the bimodal multi-zone 1R1C model in terms of prediction accuracy compared to the multi-zone 1R1C model.

Accurate modeling of power systems is crucial for various control center applications. One of the key components in a power system model is the transmission lines. The line model includes parameters such as series impedance and shunt admittance, which are typically assumed to be time-invariant. However, these parameters vary based on the ambient and operating conditions of the system. Therefore, it is essential for the different monitoring and protection applications of the power system to have an accurate visualization of the line parameter variance throughout the day. In this context, this paper develops a hybrid line parameter estimation scheme that makes use of the available Phasor Measurement Unit (PMUs) measurements and Neural Network (NN) estimations to calculate the series line parameters of the transmission lines. The developed scheme was tested in the IEEE 14-bus system under realistic ambient and operating conditions, showcasing its practical applicability and enhanced accuracy.

The increasing deployment of Distributed Energy Resources (DERs) creates an urgent need for active distribution grid management and effective coordination between the Transmission System Operator (TSO) and Distribution System Operator (DSO). Several TSO-DSO coordination models have been proposed in the literature. Two of these models, the Centralized Market and the Local Market model, require a market order prequalification stage. Prequalification is essential to ensure the distribution grid thermal limits are not violated when DER orders are activated in the central market (operated by the TSO). In this study, a market order prequalification scheme, applicable to both coordination models, is proposed to create a new set of market orders, ensuring that the distribution lines will not get congested under any order activation scenario. The effectiveness of the proposed prequalification scheme and the performance of the two coordination models are evaluated through a case study using a test system comprising the IEEE 9-bus system (transmission) and the IEEE 33-bus test feeder (distribution).

The performance of long-term power system studies are vital to evaluate the system reliability and efficiency under an increasing penetration of renewable energy sources (RES). This work develops a long-term unit commitment (UC) model considering combined-cycle (CC) units to enable the performance of annual studies in power systems. Specifically, a simplified configuration-based model of the CC units is used that decreases the number of configurations to reduce the computational complexity. The considered UC problem is formulated as a mixed-integer linear program (MILP), which is intractable when a long-term horizon is used. To make the problem tractable, a rolling horizon approach is proposed to solve the long-term MILP problem, generating high-quality approximate solutions. The proposed approach is applied to a real isolated power system and an annual study is carried out to examine the impact of an increasing RES penetration on the system operation.

State estimation is the cornerstone of the power system control center since it provides the operating condition of the system in consecutive time intervals. This work investigates the application of physics-informed neural networks (PINNs) for accelerating power systems state estimation in monitoring the operation of power systems. Traditional state estimation techniques often rely on iterative algorithms that can be computationally intensive, particularly for large-scale power systems. In this paper, a novel approach that leverages the inherent physical knowledge of power systems through the integration of PINNs is proposed. By incorporating physical laws as prior knowledge, the proposed method significantly reduces the computational complexity associated with state estimation while maintaining high accuracy. The proposed method achieves up to 11% increase in accuracy, 75% reduction in standard deviation of results, and 30% faster convergence, as demonstrated by comprehensive experiments on the IEEE 14-bus system.

Real data that are derived from two PMUs installed in different substations of a real system that includes timestamp, three phase voltage and current phasor measurements for two lines of the system, frequency measurements, and rate of change of frequency (ROCOF) measurements.