Abstract:On-shore wind parks are typically connected to the high-voltage (HV) transmission system through a bulk transformer. However, wind generators may be connected directly at a medium-voltage (MV) level, such as a utility-owned primary distribution network, if the network is capable of sustaining the power flow and ensuring adequate power quality for its users. This paper presents the findings of a comprehensive study on the management of resonance in a utility-owned wind park in Costa Rica. The wind park is connected directly to the MV primary distribution network and has no shunt capacitor for power factor correction. The results demonstrate that such configuration has a higher immunity to resonances, as the total grid equivalent impedance perceived by the wind park is typically dominated by the absent HV/MV transformer and shunt capacitor bank. Moreover, the capacitance provided by the underground feeders of the wind park did not result in natural oscillation frequencies in the range of typical harmonic distortions observed in MV distribution networks that violated power quality standards.

Abstract: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.

Abstract:With the proliferation of advanced communication technologies and the deepening interdependence between cyber and physical components, power distribution networks are subject to miscellaneous security risks induced by malicious attackers. To address the issue, this paper proposes a security risk assessment method and a risk-oriented defense resource allocation strategy for cyber-physical distribution networks (CPDNs) against coordinated cyber attacks. First, an attack graph-based CPDN architecture is constructed, and representative cyber-attack paths are drawn considering the CPDN topology and the risk propagation process. The probability of a successful coordinated cyber attack and incurred security risks are quantitatively assessed based on the absorbing Markov chain model and National Institute of Standards and Technology (NIST) standard. Next, a risk-oriented defense resource allocation strategy is proposed for CPDNs in different attack scenarios. The trade-off between security risk and limited resource budget is formulated as a multi-objective optimization (MOO) problem, which is solved by an efficient optimal Pareto solution generation approach. By employing a generational distance metric, the optimal solution is prioritized from the optimal Pareto set of the MOO and leveraged for subsequent atomic allocation of defense resources. Several case studies on a modified IEEE 123-node test feeder substantiate the efficacy of the proposed security risk assessment method and risk-oriented defense resource allocation strategy.

Abstract:Maintaining a continuous power balance is crucial for ensuring operational feasibility in power systems. However, due to forecasting difficulties and computational limitations, economic dispatch often relies on discrete interval horizons, which fail to guarantee feasibility within each interval. This paper introduces the concept of a continuous operating envelope for managing intra-interval fluctuations, delineating the range within which fluctuations remain manageable. We propose a parametric programming model to construct the envelope, represented as a polytope that accounts for both timescale and fluctuation dimensions. To address the computational challenges inherent in the parametric programming model, we develop a fast solution method to provide an approximated polytope. The approximated polytope, initially derived from lower-dimensional projections, represents a subset of the exact polytope that ensures operational feasibility. Additionally, we apply a polytope expansion strategy in the original dimensions to refine the approximated polytope, bringing the approximation closer to the exact polytope. Case studies on an illustrative 5-bus and a utility-scale 661-bus system demonstrate that the method effectively and stably provides a continuous operating envelope, particularly for high-dimensional problems.

Abstract:Lossy power flow naturally extends lossless linear power flow to lossy distribution networks, further improving the accuracy of approximate computation and analysis. However, these enhanced versions are only applicable at the alternating current (AC) transmission level, and the accuracy is limited in distribution networks, especially in hybrid AC-direct current (DC) distribution networks. In this paper, we revisit the lossy power flow model and extend it to hybrid AC-DC distribution networks with multi-terminal voltage source converters. The proposed lossy power flow model can be reformulated as an iteration problem with node power injection as the fixed point. For this purpose, a node power injection modification model based on direct derivation is proposed by exploiting the negligibility of the phase angle differences, and iteratively solving lossy power flows for both AC and DC sub-networks. For coupling devices, to guarantee that the power flow is matched on both AC and DC sides, we formulate a rigorous fixed-point problem to solve the lossy power flow of voltage source converters. Finally, the high accuracy and computational efficiency of the proposed model are verified on multiple test cases.

Abstract:DC series-parallel power flow controller (SP-PFC) is a highly efficient device to solve the problem of uncontrolled line current in the bipolar DC distribution system. However, its potential in fault current limiting is not fully explored. In this paper, a self-adaptive action strategy (SAAS) and a parameter optimization method of SP-PFC in bipolar DC distribution systems are proposed. Firstly, the common- and different-mode (CDM) equivalent circuits of the bipolar DC distribution system with SP-PFC in different fault stages are established, which avoids the line coupling inductance. Based on this, the influence of different parameters and line coupling inductance on the fault current limiting capability are investigated. It is found that the SP-PFC has the best fault current limiting capability when the capacitance and inductance of filter are inversely proportional. To realize the adaptability of fault current limiting capability under different fault severities, the SAAS of SP-PFC is proposed. The validity of the CDM equivalent circuits and parameter optimization method, and the effectiveness of the SAAS are verified by simulations and experiments.

Abstract:This paper develops a physics-guided graph network to enhance the robustness of distribution system state estimation (DSSE) against anomalous real-time measurements, as well as a deep auto-encoder (DAE)-based detector and a Gaussian process-aided residual learning (GARL) to deal with challenges arising from topology changes. A global-scanning jumping knowledge network (GSJKN) is first designed to establish the regression rule between the measurement data and state variables. The structural information of distribution system (DS) and a global-scanning module are incorporated to guide the propagation of scarce measurements in the graph topology, contributing to valid estimation precision in sparsely measured DSs. To monitor the topology changes of the network, a DAE network is employed to learn an efficient representation of the measurements of the system under a certain topology, which can achieve online monitoring of the network structure by observing the variation tendency of the reconstruction error. When the topology change occurs, a Gaussian process with a composite kernel is applied to the modeling of the pre-trained GSJKN residual to adapt to the new topology. The embedding of the physical structural knowledge enables the proposed GSJKN method to restore the missing/noisy values utilizing the adjacent measurements, which enhances the robustness to typical data acquisition errors. The adopted DAE network and special GARL-based transfer method further allow the DSSE method to rapidly detect and adapt to the topology change, as well as achieve effective quantification of the estimation uncertainties. Comparative tests on balanced and unbalanced systems demonstrate the accuracy, robustness, and adaptability of the proposed DSSE method.

Abstract:A novel planning tool for optimizing the placement of electric springs (ESs) in unbalanced distribution networks is introduced in this study. The total voltage deviation is used as the optimization criterion and is calculated when the ESs operate at their maximum reactive power either in the inductive or capacitive modes. The power rating of the ES is adjusted on the basis of the available active power at the bus. And in the optimization problem, it is expressed as the power ratio of the noncritical load (NCL) and critical load (CL). The implemented ES model is flexible, which can be used on any bus and any phase. The model determines the output voltage from the parameters and operating conditions at the point of common coupling (PCC). These conditions are integrated using the backward/forward sweep method (BFSM) and are updated during power flow calculations. The problem is described as a mixed-integer nonlinear problem and solved efficiently using an improved BFSM-based genetic algorithm, which computes power flow and ES placement simultaneously. The effectiveness of this method is evaluated through testing in IEEE 13-bus and 34-bus systems.

Abstract:The volatility of increasing distributed generators (DGs) poses a severe challenge to the supply restoration of active distribution networks (ADNs). The integration of power electronic devices represented by soft open points (SOPs) and mobile energy storages (MESs) provides a promising opportunity for rapid supply restoration with high DG penetration. Oriented for the post-event rapid restoration of ADNs, a bi-level supply restoration method is proposed considering the multi-resource coordination of switches, SOPs, and MESs. At the upper level (long-timescale), a multi-stage supply restoration model is developed for multiple resources under uncertainties of DGs and loads. At the lower level (short-timescale), a rolling correction restoration strategy is proposed to adapt to the DG and load fluctuations on short timescales. Finally, the effectiveness of the proposed method is verified based on a modified practical distribution network and IEEE 123-node distribution network. Results show that the proposed method can fully utilize the coordination potential of multiple resources to improve load restoration ratio for ADNs with DG uncertainties.

Abstract:In volt/var control (VVC) for active distribution networks, it is essential to integrate traditional voltage regulation devices with modern smart photovoltaic inverters to prevent voltage violations. However, model-based multi-device VVC methods rely on accurate system models for decision-making, which can be challenging due to the extensive modeling workload. To tackle the complexities of multi-device cooperation in VVC, this paper proposes a two-timescale VVC method based on reinforcement learning with hybrid action space, termed the hybrid action representation twin delayed deep deterministic policy gradient (HAR-TD3) method. This method simultaneously manages traditional discrete voltage regulation devices, which operate on a slower timescale, and smart continuous voltage regulation devices, which function on a faster timescale. To enable effective collaboration between the different action spaces of these devices, we propose a variational auto-encoder based hybrid action reconstruction network. This network captures the interdependencies of hybrid actions by embedding both discrete and continuous actions into the latent representation space and subsequently decoding them for action reconstruction. The proposed method is validated on IEEE 33-bus, 69-bus, and 123-bus distribution networks. Numerical results indicate that the proposed method successfully coordinates discrete and continuous voltage regulation devices, achieving fewer voltage violations compared with state-of-the-art reinforcement learning methods.

Abstract:The multi-voltage-level DC distribution network (MVL-DC-DN) is a promising network for efficiently integrating rapidly growing DC loads, and fast-growing load demand would bring a challenge to the MVL-DC-DN in terms of the maximum loadability. This paper considers the DC electric spring (DC-ES) as a novel candidate flexible resource for enhancing the maximum loadability of the MVL-DC-DN, and proposes an evaluation method for the maximum loadability. Firstly, with the consideration of device constraints, the impact that the DC-ES on the maximum loadability of the DC distribution network (DC-DN) is analyzed via a simplified equivalent circuit. Subsequently, the power flow (PF) model of an MVL-DC-DN with DC-ESs is established. Finally, a method based on continuation power flow (CPF) for evaluating the maximum loadability of an MVL-DC-DN with DC-ESs is proposed. During the evaluation, limitations of the DC-ES and the DC transformer (DCT) are considered. The consideration of the practical constraints avoids the overestimation of the maximum loadability. The case study verifies the effectiveness of the proposed method.

Abstract:With the increasing integration of uncertain distributed renewable energies (DREs) into distribution networks (DNs), communication bottlenecks and the limited deployment of measurement devices pose significant challenges for advanced data-driven voltage control strategies such as deep reinforcement learning (DRL). To address these issues, this paper proposes an offline-training online-execution framework for volt-var control in DNs. In the offline-training phase, a graph convolutional network (GCN)-based denoising autoencoder (DAE), referred to as the deep learning (DL) agent, is designed and trained to capture spatial correlations among limited physical quantities. This agent predicts voltage values for nodes with missing measurements using historical load data, DRE outputs, and global voltages from simulations. Furthermore, the dual-timescale voltage control problem is formulated as a multi-agent Markov decision process. A DRL agent employing the multi-agent soft actor-critic (MASAC) algorithm is trained to regulate the tap position of on-load tap changer (OLTC) and reactive power output of photovoltaic (PV) inverters. In the online-execution phase, the DL agent supplements the limited measurement data, providing enhanced global observations for the DRL agent. This enables precise equipment control based on improved system state estimation. The proposed framework is validated on two modified IEEE test systems. Numerical results demonstrate its ability to effectively reconstruct missing measurements and achieve rapid, and accurate voltage control even under severe measurement deficiencies.

Abstract:The probabilistic short circuit analysis provides relevant information for power system planning and power quality assessment tasks. Traditional Monte Carlo methods (TMCMs) are usually applied to consider the randomness affecting short circuit operating conditions, but they require numerous iterations to properly characterize the network conditions. This paper proposes a mixed Taguchi-based method (MTBM) as a new alternative tool to account for the uncertainties affecting the inputs of probabilistic short circuit analysis. The MTBM significantly reduces the number of iterations required to properly address the randomness of inputs (environmental conditions, pre-fault conditions, fault characteristics), and allows diversifying the representation of inputs through a quantile-based selection of their levels. The proposed method is applied to unbalanced three-phase four-wire low-voltage (LV) distribution systems with photovoltaic systems (PVSs) operating in low voltage ride- through (LVRT) during the fault. Numerical applications related to a test system are presented, and the proposed MTBM is compared with the TMCM, the unmixed Taguchi-based method (UTBM), and the point estimate method (PEM). The proposed MTBM returns values very close to those of the TMCM (with average deviations ranging from 0.01% to 3.12%) and enables a fast and accurate analysis of faulted LV distribution systems with PVSs operating in LVRT.

Abstract:Meter encoding, as a side-effect-free scheme, has been proposed to detect false data injection (FDI) attacks without significantly affecting the operation of power systems. However, existing meter encoding schemes either require encoding lots of measurements from different buses to protect a substantial proportion of a power system or are unhidden from alert attackers. To address these issues, this paper proposes a smart inverter enabled meter encoding scheme for detecting FDI attacks in distribution system state estimation. The proposed scheme only encodes the measurements from the existing programmable smart inverters. Meanwhile, this scheme can protect all the downstream buses from the encoded inverter bus. Compared with existing schemes, the proposed scheme encodes fewer meters when protecting the same number of buses, which decreases the encoding cost. In addition, by following the physical power flow laws, the proposed scheme is hidden from alert attackers who can implement the state estimation-based bad data detection (BDD). Simulation results from the IEEE 69-bus distribution system demonstrate that the proposed scheme can mislead the attacker’s state estimation on all the downstream buses from the encoded bus without arousing the attacker’s suspicion. FDI attacks that are constructed based on the misled estimated state are very likely to trigger the defender’s BDD alarm.

Abstract:This paper captures an engaging— and at times heated—Power-Globe (PG) discussion of evolving definitions of smart grid technologies. The exchange took place between December 2024 and January 2025. The primary objective of this paper is to clarify some of the ambiguities surrounding the term “ smart grid” over the past two decades, as highlighted in the spirited PG debate. “ Smart grid” has sometimes been advocated as a panacea to resolve the tension between competing objectives for the provision of electricity (specifically, making it reliable, clean, and affordable). This paper examines the term “ smart grid” in terms of raw technical functionalities, applications, and use cases, some of which may get closer than others to meeting the aspirational promises. While smart technology should expand our menu of options, it will not absolve us of the need to make hard decisions.

Abstract:Distribution networks have been experiencing significant changes under the pressure of the energy transition. The high integration of renewable energy sources combined with the electrification process introduces new challenges in managing distribution networks. Innovative solutions aimed at optimizing the control of complex problems, starting from historical data instead of a detailed system model, have been growing due to rapid development in artificial intelligence and machine learning. This paper proposes a Q-learning algorithm to control the tap setting of the on-load tap changer installed in primary substation transformers. The ultimate goal is to maintain voltage magnitudes at all busses of the medium-voltage distribution network within a safe range, simultaneously optimizing on-load tap changer operations. As a case study, the effectiveness of the proposed algorithm is assessed using a real medium-voltage distribution network with high penetration of renewable energy sources that supplies more than 2500 users/prosumers. The ability of the proposed algorithm to control bus voltages is tested in several scenarios characterized by significant variability and uncertainty. Outcomes show that the proposed algorithm is suitable for optimizing voltage control in distribution networks using a data-driven approach.

Abstract:To address the limitations of traditional planning methods in handling complex scenarios such as multi-feeder or substation cluster supply under high photovoltaic (PV) penetration, this paper proposes a collaborative configuration optimization method of soft open points (SOPs) and distributed multi-energy stations with spatiotemporal coordination and complementarity to reduce renewable energy curtailment. First, a shared strategy of multiple types of resources is proposed based on an SOP-enabled flexible distribution network. Second, a distributed hydrogen-based multi-energy coupling system (DHMECS) is developed. Then, a DHMECS siting model considering inter-feeder resource sharing is formulated. Finally, a configuration model of SOP and DHMECS is proposed, incorporating a partitioned autonomous operation strategy that considers spatiotemporal coordination and complementarity. The proposed method is validated on the improved Portugal 54-node and 219-node distribution networks, and the results demonstrate that it mitigates severe voltage violations and PV curtailment, enhances partitioned autonomous operation capabilities, and addresses the challenges of complex planning scenarios involving multi-feeder or substation cluster supply.

Abstract:This paper proposes an advanced voltage source converter (VSC)-driven model for soft open points (SOPs) and battery energy storage systems (BESSs) to actively balance three-phase distribution networks. The proposed model addresses the phase imbalance caused by the increasing integration of renewable energy and distributed generation. Unlike traditional models, which mainly focus on AC capacity constraints, the proposed model explores the complexities of the DC-link. This allows for a thorough examination of the interactions between active and reactive power, as well as the voltage levels on both the AC and DC sides of VSCs. The relationship between pulse width modulation (PWM) control configurations and VSC power outputs is discussed, enhancing control on both sides of the converters. This improvement also facilitates better cross-phase power transfer through SOPs and enhances the overall balance among the three phases. In addition, the proposed model incorporates the cooperative functionality of VSC-driven BESSs to sustain the phase balance. To further optimize the load distribution, phase-specific dispatching (PSD) is introduced, allowing for the flexible allocation of individual loads to distinct phases. Together, these coordinated technical solutions constitute a systematic optimization strategy. An algorithm is developed to harmonize the VSC-driven modeling for SOPs and BESSs with PSD, thereby improving the computational efficiency in managing power flow and phase balance. The results show that the proposed model significantly reduces losses and enhances the phase balance.

Abstract:This paper proposes a method for obtaining nonlinear models of distribution grid based on available measurements from the power grid. We formulate a maximum likelihood estimation (MLE) problem that estimates unknown line parameters—specifically, the impedance between nodes—using measured voltage magnitudes and net active and reactive power injections at each node. The nonlinear model for the distribution grid uses a nonlinear approximation of the DistFlow model, which includes line losses and is parameterized by the unknown line impedances. We solve the resulting MLE problem using an expectation maximization (EM) algorithm, tailored for the nonlinear model, and provide a numerically robust implementation. The proposed method is demonstrated on the IEEE 37-node test network, and we compare it with the state-of-the-art methods. The proposed method achieves a 70% reduction in voltage error and an error for state variables that is more than 10000 times smaller. A final comparison uses data from a real network, and the proposed method achieves parameter estimates with errors 100 times smaller than competing methods.

Abstract:The transformer-less configuration is regarded as a preferred solution for soft-open-point (SOP)-based flexible interconnected distribution networks (FIDNs). This study proposes a reliable and cost-effective method for handling single-line-to-ground (SLG) faults in transformer-less SOP-based FIDNs. Firstly, the zero-sequence fault equivalent circuit of FIDNs is established, and the fault propagation characteristics between the grids interconnected by the SOP are analyzed. Secondly, a zero-sequence current isolation strategy based on proportional-resonant control is proposed to prevent fault propagation toward the healthy-side grid. An active injection-based grounding parameter identification (GPI) approach is then proposed, enabling accurate calculation of the compensation current for arc suppression. Finally, the selection of injected signal parameters is discussed, and the complete timing flow of the SLG fault handling method is presented. A 10 kV SOP-based FIDN model configured with a transformer-less topology is developed in PSCAD/EMTDC. The performance of the SLG fault handling method is verified under transition resistances ranging from 10 Ω to 10 kΩ. The results reveal that the average relative error in GPI is less than 4%. Furthermore, the compensated currents of faulty branch remain below 10 A across various fault conditions.