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 deployment of voltage source converters (VSC) to facilitate flexible interconnections between the AC grid, renewable energy system (RES) and Multi-terminal DC (MTDC) grid is on the rise. However, significant challenges exist in exploiting coordinated operations for such AC/VSC-MTDC hybrid power systems. One of the most critical issues is how to achieve the optimal operation of such wide-area systems involving several power entities with as minimal communication burden as possible. To address this issue, an enhanced AC/DC optimal power flow (OPF) is specifically proposed. Firstly, a mixed-integer convex AC/DC OPF model is explicitly formulated to describe the optimal operation of such hybrid power systems. Subsequently, a nested distributed optimization method with double iteration loops is developed to offer optimal system-wide decision-making through a more “thorough” distributed communication architecture. In the outer iteration, the original AC/DC OPF problem is decomposed into several slave problems (SPs) associated with systems (including the AC grid and RESs) and one master problem (MP) associated with the integrated VSC-MTDC grid. Generalized Benders decomposition (GBD) serves to solve the master and slave problems iteratively. Techniques such as multi-cut generation and asynchronous updating are utilized to upgrade the GBD performance of computation efficiency and address communication delays. In the inner iteration, the master problem is continuously decomposed into multiple sub-MPs associated with individual VSCs. The alternating direction method of multipliers (ADMM) is employed to solve these sub-MPs iteratively. Proximal terms and heuristic approaches are embedded to enable parallel computation and handling of integer variables. Numerical experiment results finally validate the effectiveness of the proposed enhanced AC/DC OPF. The constructed AC/DC OPF model exhibits acceptable accuracy in terms of power flow calculation, and the developed nested distributed optimization method showcases decent convergence rate and solution optimality performances.

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This work seeks to reduce the severity of congestion in the medium voltage (MV) cyber-physical systems (CPES) by optimising the network topology in line with seasonal variations in the supply and demand of electricity. To this aim a two-stage reconfiguration algorithm is proposed. In the first stage, the positions of the normally open switches within the network are optimised in to adjust the power flow therein. These optimised positions are subsequently used in the second stage to calculate the network variables. The first stage is implemented as a mixed-integer linear program (MILP) optimisation in Python, whereas the second stage consists of a Newton-Raphson calculation in DIgSILENT PowerFactory software package. The benefit of network reconfiguration is that it is a short-term and low-cost solution which can be implemented by a distribution system operator (DSO) without relying on other external parties. The presented case study addresses the need of seasonal reconfigurations to accommodate for the manual operation of the switches present within a synthetic model of MV CPES, which is implemented inspired from CPES in the Netherlands. The application of the algorithm significantly reduces the frequency by which reconfiguration actions can be performed. Furthermore, the algorithm is able to consistently reduce congestion within the analysed synthetic CPES, completely removing it or reducing its severity. It also outperforms two alternative optimisation options implemented in PowerFactory with regard to the objective function value. Those being an iterative exploration of meshes and a genetic algorithm.

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Under new EU regulation, as of 2035 all new cars and vans registered in the EU are set to be zero-emission. This ambitious target will be an important driver for a large-scale rollout of e-mobility across European cities. To ensure the successful planning of the energy infrastructure and optimized operation of the expanding e-mobility systems, smart and robust tools will be in high demand for various stakeholders, such as industry, city planners, or researchers. As part of the EU-funded research and innovation project DriVe2X, an interactive open-source prediction tool for Vehicle-to-Grid (V2G) operation and planning has been conceptualized and is under development. The main functions of this tool are the prediction of energy balance and flexibility at the public charging station level for a day-ahead operation and providing useful customized analytics for long-term planning purposes.@en

This paper investigates the impact of adaptive activation functions on deep learning-based power flow analysis. Specifically, it compares four adaptive activation functions with state-of-the-art activation functions, i.e., ReLU, LeakyReLU, Sigmoid, and Tanh, in terms of loss function error, convergence speed, and learning process stability, using a real-world distribution network dataset. Results indicate that the proposed adaptive activation functions improve learning capability over state-of-the-art activation functions. Notably, adaptive ReLU activation shows the most improved learning process, with convergence speed up to twice as fast as ReLU. Adaptive Sigmoid and Tanh activation functions exhibit superior performance in terms of loss function error, outperforming ReLU and LeakyReLU by up to two orders of magnitude. Furthermore, the proposed adaptive activation functions lead to smoother and more stable learning processes, especially during early training, improving convergence. The practical implications of this study include the potential application of these adaptive activation functions in distribution network modeling, forecasting, and control, leading to more accurate and reliable power system operation.

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The heterogeneous distribution of frequency support from dispersed renewable generation sources results in varying inertia within the system. The effects of disturbances exhibit non-uniform variations contingent upon the disturbance's location and the affected region's topology and inertia. A screening method for inertia-zone identification is proposed considering the combination of network structure and generator inertia distribution that will aid in comprehending the response of nodes to disturbances. The nodes' dynamic nodal weight (DNW) is defined using maximal entropy random walk that defines each node's spreading power dynamics. Further, a modified weighted kmeans++ clustering technique is proposed using DNW to obtain the equivalent spatial points of each zone and the system to parameterize the inertia status of each zone. The impact of the proposed scheme is justified by simulating a modified IEEE 39 bus system with doubly-fed induction generator (DFIG) integration in the real-time digital simulator.

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Interest in integrating distributed energy resources (DERs) into the electric distribution system (EDS) is growing due to the economic and operational benefits that DERs can provide. Consumer-sited photovoltaic (PV) generation is one of those DERs that have been penetrating the EDS over the years; the owner can sell the energy surplus to the EDS, and for the EDS operator it brings the advantage of clean energy from solar irradiation; since the EDS operator doesn't have control of it, the growing of the penetration can leading into technical problems to the EDS. Energy storage systems (ESS) can be used to manage non-dispatchable renewable energy providing ancillary services to the EDS like peak shaving and voltage regulation. However, investors for the ESS planning (allocation and sizing) must consider their cost, EDS constraints, and the future scenarios of PV penetration and uncertainties inherent to the EDS; for this purpose, a stochastic mixed integer linear programming model for the ESS planning is presented integrating a temporal method to model the PV penetration growth over the years. The decision variables are the size and the EDS node where the ESS should be connected. The model is implemented in AMPL and the optimal solution is found with CPLEX for a case study based on the IEEE 33 node test system.

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Accurate and efficient power flow (PF) analysis is crucial in modern electrical networks’ operation and planning. Therefore, there is a need for scalable algorithms that can provide accurate and fast solutions for both small and large scale power networks. As the power network can be interpreted as a graph, Graph Neural Networks (GNNs) have emerged as a promising approach for improving the accuracy and speed of PF approximations by exploiting information sharing via the underlying graph structure. In this study, we introduce PowerFlowNet, a novel GNN architecture for PF approximation that showcases similar performance with the traditional Newton–Raphson method but achieves it 4 times faster in the IEEE 14-bus system and 48 times faster in the realistic case of the French high voltage network (6470rte). Meanwhile, it significantly outperforms other traditional approximation methods, such as the DC power flow, in terms of performance and execution time; therefore, making PowerFlowNet a highly promising solution for real-world PF analysis. Furthermore, we verify the efficacy of our approach by conducting an in-depth experimental evaluation, thoroughly examining the performance, scalability, interpretability, and architectural dependability of PowerFlowNet. The evaluation provides insights into the behavior and potential applications of GNNs in power system analysis.

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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

Optimizing operational set points for modular multilevel converters (MMCs) in Multi-Terminal Direct Current (MTDC) transmission systems is crucial for ensuring efficient power distribution and control. This paper presents an enhanced Optimal Power Flow (OPF) model for MMC-MTDC systems, integrating a novel adaptive voltage droop control strategy. The strategy aims to minimize generation costs and DC voltage deviations while ensuring the stable operation of the MTDC grid by dynamically adjusting the system operation points. The modified Nordic 32 test system with an embedded 4-terminal DC grid is modeled in Julia and the proposed control strategy is applied to the power model. The results demonstrate the feasibility and effectiveness of the proposed droop control strategy, affirming its potential value in enhancing the performance and reliability of hybrid AC-DC power systems.

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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

By 2050, there will be more than 1 billion electric vehicles (EVs) on the road. This presents an opportunity for Europe to rapidly expand its share of variable renewable energy (VRE) generation, which is in line with major climate policies at European Union (EU) level. Despite that promising outlook, the rapid growth of electric mobility will pose major technical challenges and infrastructural risks to Europe’s power system. One way to address this is through widespread implementation of smart bidirectional EV charging, which can potentially provide the power system with the flexibility to accommodate higher shares of VREs. The implementation scenarios for this technology are collectively known as vehicle-to-everything, or V2X, including vehicle-to grid (V2G), vehicle-to-home (V2H), and vehicle-to-building (V2B). This paper summarizes recent developments in the V2X arena, doing it in three stages. Firstly, it focuses on taxonomy aspects, performing a bibliometric analysis and studying technical term conventions. Secondly, it compiles relevant barriers to implementation and maps key issues addressed in scientific literature. And thirdly, it surveys past and existing projects to retrieve updated insights on its technical and market orientations. This review is produced as part of the EU-funded DriVe2X project, which develops new expert knowledge and technical solutions to help cope with the forthcoming mass EV deployment in Europe.@en

The power generation and consumption of distributed energy resources (DERs) offer significant flexibility potential, which can be utilized to provide services such as peak and frequency regulation. DERs introduce a vast number of variables and constraints, making it complicated to directly integrate them into upper-level dispatch. To address this challenge, virtual power plants (VPPs) emerge, which treat diverse DERs as a collective entity and use aggregated flexibility envelopes to reduce the variable and constraint scale, facilitating upper-level optimization. In VPPs, unified DER modeling and efficient DER aggregation play a crucial role but are challenging. This paper first introduces a novel unified polytope model to represent heterogenous DERs' flexibility region. A coordination transformation is utilized to eliminate redundant variable dimensions and maintain DERs' interface characteristics. A sample-based projection method is then developed, further removing all state variables, resulting in a unified flexibility region. This method is then utilized to calculate the Minkowski sums of individual flexibility polytopes for aggregation. The results of numerical tests demonstrate a considerable reduction in computation time and maintain satisfactory accuracy when the proposed modeling and aggregation approach is adopted.

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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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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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This article proposes a framework to identify, visualize, and quantify risk of potential over/under voltage due to annual energy consumption and PV generation growth. The stochastic modeling considers the following: (i) Active and reactive power profiles for distribution transformers, dependent on annual energy consumption and activity in the serviced areas. (ii) Variable solar irradiance profiles that allow a broader range of PV generation scenarios for sunny, overcast, and cloudy days. The proposed framework uses multivariate-t copulas to model temporal correlations between random variables to generate synthetic scenarios. A probabilistic power flow is computed using the generated scenarios to define critical static operating regions. Results show that classical approaches may underestimate the maximum PV capacity of distribution networks when local irradiance conditions are not considered. Moreover, it is found that including annual energy consumption growth is critical to establishing realistic PV installation capacity limits. Finally, a sensitivity analysis shows that taking a 5% of overvoltage risk could increase up to 15% of the PV installed capacity limits.

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Traditionally, Volt/VAR optimization (VVO) is performed in distribution networks through legacy devices such as on-load tap changers (OLTCs), voltage regulators (VRs), and capacitor banks. With the amendment in IEEE 1547 standard, distributed energy resources (DERs) can now provide reactive power support to the grid. For this, renewable energy-based DERs, such as PV, are interfaced with the distribution networks through smart inverters (SIs). Due to the intermittent nature of such resources, VVO transforms into a dynamic problem that requires extensive communication between the VVO controller and devices performing the VVO scheme. This communication, however, can be potentially tampered with by an adversary rendering the VVO ineffective. In this regard, it is important to assess the impact of cyberattacks on the VVO scheme. This paper develops a real-time co-simulation setup to assess the effect of cyberattacks on VVO. The setup consists of a real-time power system simulator, a communication network emulator, and a master controller in a system-in-the-loop (SITL) setup. The DNP3 communication protocol is adopted for the underlying communication infrastructure. The results show that corrupted communication messages can lead to violation of voltage limits, increased number of setpoint updates of VRs, and economic loss.

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

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