The integration of distributed energy resources (DERs) has escalated the challenge of voltage magnitude regulation in distribution networks. Model-based approaches, which rely on complex sequential mathematical formulations, cannot meet the real-time demand. Deep reinforcement learning (DRL) offers an alternative by utilizing offline training with distribution network simulators and then executing online without computation. However, DRL algorithms fail to enforce voltage magnitude constraints during training and testing, potentially leading to serious operational violations. To tackle these challenges, we introduce a novel safe-guaranteed reinforcement learning algorithm, the DistFlow safe reinforcement learning (DF-SRL), designed specifically for real-time voltage magnitude regulation in distribution networks. The DF-SRL algorithm incorporates a DistFlow linearization to construct an expert-knowledge-based safety layer. Subsequently, the DF-SRL algorithm overlays this safety layer on top of the agent policy, recalibrating unsafe actions to safe domains through a quadratic programming formulation. Simulation results show the DF-SRL algorithm consistently ensures voltage magnitude constraints during training and real-time operation (test) phases, achieving faster convergence and higher performance, which differentiates it apart from (safe) DRL benchmark algorithms.

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Deep Reinforcement Learning (DRL) presents a promising avenue for optimizing Energy Storage Systems (ESSs) dispatch in distribution networks. This paper introduces RL-ADN, an innovative open-source library specifically designed for solving the optimal ESSs dispatch in active distribution networks. RL-ADN offers unparalleled flexibility in modeling distribution networks, and ESSs, accommodating a wide range of research goals. A standout feature of RL-ADN is its data augmentation module, based on Gaussian Mixture Model and Copula (GMC) functions, which elevates the performance ceiling of DRL agents, achieving an average performance improvement of 21.43%, 1.08%, 2.76%, by augmenting five-year, one-year and three-month data, respectively. Additionally, RL-ADN incorporates the Tensor Power Flow solver, significantly reducing the computational burden of power flow calculations during training without sacrificing accuracy, maintaining voltage magnitude with an average error not exceeding 0.0001%. The effectiveness of RL-ADN is demonstrated using distribution networks with size varying, showing marked performance improvements in the adaptability of DRL algorithms for ESS dispatch tasks. Furthermore, RL-ADN achieves a tenfold increase in computational efficiency during training, making it highly suitable for large-scale network applications. The library sets a new benchmark in DRL-based ESSs dispatch in distribution networks and it is poised to advance DRL applications in distribution network operations significantly. RL-ADN is available at: https://github.com/ShengrenHou/RL-ADN and https://github.com/distributionnetworksTUDelft/RL-ADN.

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The topology of low-voltage distribution networks (LVDNs) is crucial for system analysis, e.g., distributed energy resources (DERs) integration, network hosting capacity analysis, state estimation, and electric vehicle charging management. However, it is frequently unavailable or incomplete. This paper develops a data-driven topology identification approach for LVDNs with a high proportion of underground cables. The proposed approach exploits the fact that underground cables usually follow the street pattern, thus relying on open street map (OSM) and smart meter (SM) data. Three stages compose the proposed approach: In the first stage, a hierarchical minimum spanning tree algorithm is proposed to generate the initial topology with an accurate number of sub-branches from the pre-processed OSM data and peak demand. In the second stage, based on the limited SM data, the location of breakpoints in mesh topology caused by circle roads is verified and reconstructed to guarantee the radial structure of LVDNs. Finally, given multiple incomplete SM datasets, three data-driven optimization models based on a state estimation model are constructed to mitigate the error of cable length induced by OSM data. The feasibility of the proposed topology identification approach is verified on three actual LVDNs in The Netherlands and multiple incomplete SM datasets. Furthermore, the minimal amount of SM data needed to minimize the error of cable length is analyzed.

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Electrical power grids are vulnerable to cyber attacks, as seen in Ukraine in 2015, 2016, and 2022. These cyber attacks are classified as Advanced Persistent Threats (APTs) with potential disastrous consequences such as a total blackout. However, state-of-the-art intrusion detection systems are inadequate for APT detection owing to their stealthy nature and long-lasting persistence. Furthermore, they are ineffective as they focus on individual anomaly instances and overlook the correlation between attack instances. Therefore, this research proposes a novel method for spatio-temporal APT detection and correlation for cyber-physical power systems. It provides online situational awareness for power system operators to pinpoint system-wide anomaly locations in near real-time and preemptively mitigate APTs at an early stage before causing adverse impacts. We propose an Enhanced Graph Convolutional Long Short-Term Memory (EGC-LSTM) by using sequential and neural network filters to improve APT detection, correlation, and prediction. Control center and substation communication traffic is used to determine cyber anomalies using semi-supervised deep packet inspection and software-defined networking. Power grid circuit breaker status is used to determine physical anomalies. Cyber-physical anomalies are correlated in cyber-physical system integration matrix and EGC-LSTM. The EGC-LSTM outperforms existing state-of-the-art spatio-temporal deep learning models, achieving the lowest mean square error.@en

Residential load profiles (RLPs) play an increasingly important role in the optimal operation and planning of distribution systems, particularly with the rising integration of low-carbon energy resources such as PV systems, electric vehicles, small-scale batteries, etc. Despite the prevalence of various data-driven models for generating consumption profiles, there is a lack of clear conclusions about their relative strengths and weaknesses. This study undertakes a comprehensive comparison of frequently used data-driven models in recent research, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAE), Wasserstein GANs (WGAN), WGANs with Gradient Penalty (WGANGP), Gaussian Mixture Models (GMMs), and Gaussian Mixture Copulas (GMC). The presented comparison explores the effectiveness of the above-mentioned models on transformer- and consumer-level consumption profiles, as well as for different time resolutions (15-min, 30-min, and 60-min). The objective of this research is to elucidate the respective advantages and drawbacks of these models, thereby providing valuable insights for subsequent research in this field.

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Power flow (PF) analysis is a foundational computational method to study the flow of power in an electrical network. This analysis involves solving a set of non-linear and non-convex differential-algebraic equations. State-of-the-art solvers for PF analysis, therefore, face challenges with scalability and convergence, specifically for large-scale and/or ill-conditioned cases characterized by high penetration of renewable energy sources, among others. The adiabatic quantum computing paradigm has been proven to efficiently find solutions for combinatorial problems in the noisy intermediate-scale quantum (NISQ) era, and it can potentially address the limitations posed by state-of-the-art PF solvers. For the first time, we propose a novel adiabatic quantum computing approach for efficient PF analysis. Our key contributions are (i) a combinatorial PF algorithm and a modified version that aligns with the principles of PF analysis, termed the adiabatic quantum PF algorithm (AQPF), both of which use Quadratic Unconstrained Binary Optimization (QUBO) and Ising model formulations; (ii) a scalability study of the AQPF algorithm; and (iii) an extension of the AQPF algorithm to handle larger problem sizes using a partitioned approach. Numerical experiments are conducted using different test system sizes on D-Wave’s Advantage™ quantum annealer, Fujitsu’s digital annealer V3, D-Wave’s quantum-classical hybrid annealer, and two simulated annealers running on classical computer hardware. The reported results demonstrate the effectiveness and high accuracy of the proposed AQPF algorithm and its potential to speed up the PF analysis process while handling ill-conditioned cases using quantum and quantum-inspired algorithms.@en

The virtual integration of geographically distributed Research Infrastructures (RIs) for joint experiments in the domain of power and energy systems poses numerous challenges, particularly in terms of tool compatibility and user-friendliness. To address some of these challenges, this work presents the development and implementation of a laboratory-based middleware and data exchange service as part of the H2020 ERIGrid 2.0 project. The middleware comprises a suite of shared software tools and services designed to seamlessly integrate RIs including transport protocols as well as interface semantics. Specifically, this work details the development of a simplified and standardised interface known as the Universal Application Programming Interface (UAPI). It eliminates the need for users to grapple with the diverse intricacies of each individual RI, offering instead a tool-agnostic and standardised interface for conducting joint experiments. The work also presents and discusses the results of a real-world case study of a geographically distributed, sector-coupling experiment conducted between laboratories in Denmark, Greece, Italy, Netherlands, and Norway utilising the developed middleware.

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To enhance the economic viability of integrated energy systems, it is important to balance risk and reward while ensuring operational flexibility and compliance with regulatory constraints. Developing an integrated risk measurement method for energy trading in energy markets that complies with regulatory constraints adds another layer of complexity, especially when dealing with large-scale heterogeneous energy systems, such as those dominated by green hydrogen. Ensuring adherence to market regulations, grid codes, and flexible requirements requires a thorough understanding of legal frameworks and policy considerations, as well as integrating regulatory constraints into the bidding/offering strategies. To address these challenges and enable the profitable development of integrated electricity-hydrogen energy systems (IEHSs), this paper develops a risk-constrained bidding/offering self-scheduling strategy for IEHSs that allows for simultaneous trading in day-ahead energy and reserve electricity markets. The developed self-scheduling strategy regulates hydrogen and power flow in a centralized manner, taking into account the operational limits of the power sector, hydrogen fueling stations (HFSs), electric vehicle parking lots, fuel cell vehicle parking lots, and renewable energy sources. As a salient feature, the important human-related factors in the mobility sector are included in the proposed strategy to motivate private owners to collaborate with IEHSs and to accommodate the charging demand of vehicles with electricity market signals. To obtain the robust day-ahead schedule, economic risks associated with renewable power generation, calls for reserve deployment, and electricity market prices are taken into consideration. Towards this end, a stochastic adaptive robust optimization approach is proposed to control the risk of profit variability, which is solved by a nested column-and-constraint generation algorithm. The effectiveness and feasibility of the proposed strategy are verified using the modified IEEE 14-bus test system, as a standard scale of local IEHSs. The numerical results highlight the synergies impact between HFSs and the power sector, resulting in an increase in the profit of the IEHS from $101.654k to $275.078k. Furthermore, the IEHS can improve its profit margin and competitiveness by employing the tri-level stochastic adaptive robust optimization approach. This approach ensures that the IEHS is resistant to sudden changes in real-time interactions, even with an 8.14% increase in the imbalance cost.@en

Cyber actors can target the unsecured IEC 61850 protocols in digital substations to open circuit breakers and affect the power system operation. Thus, system operators must detect cyber-physical anomalies and differentiate in real-time between power system faults and cyber attacks on digital substations for effective incident response. In this work, we propose a novel image encoding method for event correlation using cyber-physical time-series data, i.e., Phasor Measurement Units (PMUs) and Operational Technology (OT) network traffic. More specifically, we propose a dynamic variation of the Gramian Angular Field method, which generates image streams capturing in real-time the spatial-temporal features in PMU measurements and IEC 61850 GOOSE traffic throughput. The proposed method for cyber-physical event correlation uses an image fusion technique. The method is tested using the benchmark IEEE 9-bus system. It successfully distinguishes between three-phase faults and GOOSE cyber attacks, demonstrating its usefulness for power system cyber security analytics.

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It is challenging to determine the active filter control factors in wind power plants (WPP) to obtain effective harmonic voltage mitigation and avoid over-modulation or system instability problems caused by overlarge feedforward or feedback gains. To address this issue, an active filter tuning (AFT) method is proposed in this paper to offer a common parameter tuning and stability assessment strategy for both current-controlled and voltage-controlled harmonic impedance reshaping (HIR) methods by introducing a coordination factor including different weight coefficients for system stability margin, wind turbine (WT) harmonic suppression, and grid harmonic mitigation. The superiority of the proposed method is verified in a 20 MW WPP with four cases considering both WT harmonic voltage amplification and grid voltage amplification. Compared to previous methods, the AFT-based HIR method achieves a more flexible balance between system stability and harmonic voltage mitigation, obtaining better performance in WT harmonic suppression, grid harmonic suppression, system robustness, and transient response.

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One crucial aspect of Modular Multi-level Converter (MMC)- Bipolar Point-to-Point (BPP) configuration systems is the occurrence and damping of oscillations on the DC side of HVDC networks. These oscillations can arise due to various factors, including the interaction between the AC and DC systems, de-blocking of converters after a fault, and the dynamic behaviour of connected power sources. Various investigations consider damping methods that delve to mitigate oscillatory tendencies and establish stability during Post-Fault (PF) recovery. However, the current research on damping predominantly focus on the impact of AC fault or unbalanced conditions on the DC side. This paper presents an investigation that addresses the gap concerning with sub-synchronous oscillations occurring during the de-blocking of a MMC-BPP within the post-DC fault recovery. The investigation also considers active damping and enhanced current control loop as a combined mitigation measure. A meticulous real-time digital simulation supported parametric sensitivity analysis is conducted on a four-terminal MMC-BPP synthetic power system. Numerical results provide insight into the level of effectiveness that can be achieved by the considered concept of active damping.

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This paper explores the potential application of quantum and hybrid quantum–classical neural networks in power flow analysis. Experiments are conducted using two datasets based on 4-bus and 33-bus test systems. A systematic performance comparison is also conducted among quantum, hybrid quantum–classical, and classical neural networks. The comparison is based on (i) generalization ability, (ii) robustness, (iii) training dataset size needed, (iv) training error, and (v) training process stability. The results show that the developed hybrid quantum–classical neural network outperforms both quantum and classical neural networks, and hence can improve deep learning-based power flow analysis in the noisy-intermediate-scale quantum (NISQ) and fault-tolerant quantum (FTQ) era.@en

Power grid digitalization introduces new vulnerabilities and cyber security threats. The impact of cyber attacks on power system stability is a topic of growing concern, which is yet to be comprehensively analyzed. Traditional power system stability analysis is based on the impact of non-malicious small, and large physical disturbances. However, cyber attacks introduce a new dimension to power system stability, in which malicious cyber actors can selectively target critical systems and applications and cause severe stability issues. Hence, in this work, the traditional disturbances considered in power system stability classification are expanded from physical to cyber-physical disturbances caused by cyber attacks. Based on a thorough state-of-the-art, an analysis of how cyber attacks can translate into physical disturbances affecting the traditional power system stability categories is performed. The system stability analysis is expanded by mapping the power system stability categories with the defined cyber-physical attack types. The findings of this work showcase the importance of cyber security for power system stability.@en

Electrical power systems are witnessing a paradigm shift from traditional synchronous generators towards an in-creased integration of power electronic interfaced (PEl) generation. As the global community leans towards renewables, ensuring grid stability during this transformation becomes paramount. This paper presents a fundamental study of oscillatory stability dynamics for three emerging grid-forming converter controller topologies: Virtual Synchronous Machine (VSM), droop control, and the Synchroverter, in comparison to conventional syn-chronous generation. Utilizing the two area 4 generator (2A4G) system for analysis, the research underscores the significance of converter integration, proximity-based enhancements in damping capabilities, and the delicate equilibrium in parameter tuning for optimal stability. The results pave the way for informed decision-making in grid development and renewable energy com-prehension, highlighting the potential of grid-forming converter controllers in steering a sustainable energy future.

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The increasing proportion of renewable energy introduces both long-term and short-term uncertainty to power systems, which restricts the implementation of energy management systems (EMSs) with high dependency on accurate prediction techniques. A hierarchical online EMS (HEMS) is proposed in this paper to economically operate the Hybrid hydrogen–electricity Storage System (HSS) in a residential microgrid (RMG). The HEMS dispatches an electrolyzer-fuel cell-based hydrogen energy storage (ES) unit for seasonal energy shifting and an on-site battery stack for daily energy allocation against the uncertainty from the renewable energy source (RES) and demand side. The online decision-making of the proposed HEMS is realized through two parallel fuzzy logic (FL)-based controllers which are decoupled by different operating frequencies. An original local energy estimation model (LEEM) is specifically designed for the decision process of FL controllers to comprehensively evaluate the system status and quantify the electricity price expectation for the HEMS. The proposed HEMS is independent of RES prediction or load forecasting, and gives the optimal operation for HSS in separated resolutions: the hydrogen ES unit is dispatched hourly and the battery is operated every minute. The performance of the proposed method is verified by numerical experiments fed by real-world datasets. The superiority of the HEMS in expense-saving manner is validated through comparison with PSO-based day-ahead optimization methods, fuzzy logic EMS, and rule-based online EMS.

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Synthetic networks aim to generate realistic projections of real-world networks while concealing the actual system information. Researchers have mainly explored methods to create synthetic power systems. However, with the rapid power grid digitalization, new methods are needed for synthetic communication networks of cyber–physical power systems (CPPS). In this article, we propose a two-stage generative model for generating synthetic communication topologies of large-scale CPPS based on the existing power grids. It reproduces the existing communication network design process and is capable of generating statistically realistic networks. The proposed method is implemented to create a realistic, large-scale synthetic CPPS for the interconnected power grids in continental Europe. The method is validated by comparing the generated communication network with 18 realistic communication network topologies with different system sizes. The experimental results validate the scalability and effectiveness of the generative model.@en

Probabilistic modelling of power systems operation and planning processes depends on data-driven methods, which require sufficiently large datasets. When historical data lacks this, it is desired to model the underlying data generation mechanism as a probability distribution to assess the data quality and generate more data, if needed. Kernel density estimation (KDE) based models are popular choices for this task, but they fail to adapt to data regions with varying densities. In this paper, an adaptive KDE model is employed to circumvent this, where each kernel in the model has an individual bandwidth. The leave-one-out maximum log-likelihood (LOO-MLL) criterion is proposed to prevent the singular solutions that the regular MLL criterion gives rise to, and it is proven that LOO-MLL prevents these. Relying on this guaranteed robustness, the model is extended by adjustable weights for the kernels. In addition, a modified expectation–maximization algorithm is employed to accelerate the optimization speed reliably. The performance of the proposed method and models are exhibited on two power systems datasets using different statistical tests and by comparison with Gaussian mixture models. Results show that the proposed models have promising performance, in addition to their singularity prevention guarantees.

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Cascading failures in power systems are extremely rare occurrences caused by a combination of multiple, low probability events. The looming threat of cyberattacks on power grids, however, may result in unprecedented large-scale cascading failures, leading to a blackout. Therefore, new analysis methods are needed to study such cyber induced phenomena. In this article, we propose a data-driven method for dynamical analysis of power system cascading failures caused by cyberattacks. We provide experimental proof on how attacks may accelerate the cascading failure mechanism, in comparison to historically observed blackouts. Using a dynamic power grid model, consisting of multiple, coordinated protection schemes, we define and analyze the point of no return in a cascading failure sequence by applying the Hilbert-Huang transform for time-frequency analysis. Numerical results indicate, cyberattacks may accelerate cascading failures at least by a factor of 3x. This is due to the excitation and non-damping of multiple frequency modes greater than 1 Hz in a short time span. The proposed method is tested using time domain simulations conducted through a modified IEEE 39-bus test system, which can simulate cascading outages using coordinated protection schemes.

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Power systems are undergoing rapid digitalization. This introduces new vulnerabilities and cyber threats in future Cyber-Physical Power Systems (CPPS). Some of the most notable incidents include the cyber attacks on the power grid in Ukraine in 2015, 2016, and 2022, which employed Advanced Persistent Threat (APT) strategies that took several months to reach their objectives and caused power outages. This highlights the urgent need for an in-depth analysis of APTs on CPPS. However, existing frameworks for analyzing cyber attacks, i.e., MITRE ATT&CK ICS and Cyber Kill Chain, have limitations in comprehensively analyzing APTs in CPPS environments. To address this gap, we propose a novel Advanced Cyber-Physical Power System (ACPPS) kill chain framework. The ACPPS kill chain identifies the APT characteristics that are unique to power systems. It defines and examines the cyber-physical APT stages spanning from the initial phases of infiltration to cascading failures and a power system blackout. The proposed ACPPS kill chain is validated with real-world APT attacks on the power grid in Ukraine in 2015 and 2016, and cyber-physical simulations.

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As electric vehicle (EV) numbers rise, concerns about the capacity of current charging and power grid infrastructure grow, necessitating the development of smart charging solutions. While many smart charging simulators have been developed in recent years, only a few support the development of Reinforcement Learning (RL) algorithms in the form of a Gym environment, and those that do usually lack depth in modeling Vehicle-to-Grid (V2G) scenarios. To address the aforementioned issues, this paper introduces EV2Gym, a realistic simulator platform for the development and assessment of small and large-scale smart charging algorithms within a standardized platform. The proposed simulator is populated with comprehensive EV, charging station, power transformer, and EV behavior models validated using real data. EV2Gym has a highly customizable interface empowering users to choose from pre-designed case studies or craft their own customized scenarios to suit their specific requirements. Moreover, it incorporates a diverse array of RL, mathematical programming, and heuristic algorithms to speed up the development and benchmarking of new solutions. By offering a unified and standardized platform, EV2Gym aims to provide researchers and practitioners with a robust environment for advancing and assessing smart charging algorithms.

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