Abstract:The continuous power supply system, which eliminates the neutral section and realizes safe and reliable operation, shows a development trend in suburban railways. However, the access of a power quality compensator (PQC) may alter the impedance characteristics of the system and introduce additional harmonics with a broader frequency band, potentially increasing the risk of resonance. Accordingly, in this paper, an analytical method is first adopted in conjunction with a field test to construct a simplified harmonic model for an actual continuous suburban line. A modal scanning algorithm is then used to analyze the effects of the controller and filter in the PQC on the harmonic resonance of the suburban railway continuous power supply system. Based on the improved particle swarm optimization algorithm, a multi-objective optimization design for PQC is proposed that can suppress harmonic resonance, filter the harmonics, and reduce the cost while preserving the stability of the control system. Finally, a real case study based on the field test demonstrates the effectiveness of the proposed design.

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: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:In the context of large-scale photovoltaic integration, flexibility scheduling is essential to ensure the secure and efficient operation of distribution networks (DNs). Recently, deep reinforcement learning (DRL) has been widely applied to scheduling problems. However, most methods neglect the vulnerability of DRL to state adversarial attacks such as load redistribution attacks, significantly undermining its security and reliability. To this end, a flexibility scheduling method is proposed based on robust graph DRL (RoGDRL). A flexibility gain improvement model considering temperature-dependent resistance is first proposed, which considers weather factors as additional variables to enhance the precision of flexibility analysis. Based on this, a state-adversarial two-player zero-sum Markov game (SA-TZMG) model is proposed, which converts the robust DRL scheduling problem into a Nash equilibrium problem. The proposed SA-TZMG model considers the physical constraints of state attacks that guarantee the maximal flexibility gain for the defender when confronted with the most sophisticated and stealthy attacker. A two-stage RoGDRL algorithm is proposed, which introduces the graph sample and aggregate (GraphSAGE) driven soft actor-critic to capture the complex feature about the neighbors of nodes and their properties via inductive learning, thereby solving the Nash equilibrium policies more efficiently. Simulations based on the modified IEEE 123-bus system demonstrates the efficacy of the proposed method.

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:The electrical array reconfiguration (EAR) method has become a promising solution to enhance photovoltaic (PV) system performance under partial shading conditions. Existing studies focus on maximizing single-period PV generation but neglect the impact of power fluctuation on grid stability. To address this, we propose a multi-period EAR method for multi-PV systems considering net power fluctuation mitigation. First, we design a multi-period EAR model to maximize total revenue by balancing electricity sales and net power fluctuation penalties, formulated as a stochastic mixed-integer quadratic programming problem. The model incorporates constraints on the average number of switching actions per unit time to ensure practical implementation. Then, to handle the unpredictability of partial shading conditions, we develop a Lyapunov optimization-based online algorithm to decouple the time-coupling constraints involving state transitions. Additionally, we propose a reduced set of EAR strategies to improve the computational efficiency. Numerical studies demonstrate that the proposed method significantly reduces net power fluctuations in distribution networks with high PV penetration rate and enhances total revenue compared with conventional 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.

Abstract:With the integration of wind power, photovoltaic power, gas turbine, and energy storage, the novel battery charging and swapping station (NBCSS) possesses significant operational flexibility, which can aid in the service restoration of distribution system (DS) during power outages caused by extreme events. This paper presents an integrated optimization model for DS restoration that considers NBCSS, repair crews, and network reconfigurations simultaneously. The objective of this model is to maximize the restored load while minimizing generation costs. To address the uncertainties associated with renewable energies, a two-stage stochastic optimization framework is employed. Additionally, copula theory is also applied to capture the correlation between the output of adjacent renewable energies. The conditional value-at-risk (CVaR) measure is further incorporated into the objective function to account for risk aversion. Subsequently, the proposed optimization model is transformed into a mixed-integer linear programming (MILP) problem. This transformation allows for tractable solutions using commercial solvers such as Gurobi. Finally, case studies are conducted on the modified IEEE 33-bus and 69-bus DSs. The results illustrate that the proposed method not only restores a greater load but also effectively mitigates uncertainty risks.

Abstract:Considering the majority of electrical equipment utilized in society is driven by DC, integrating a DC system can significantly enhance the efficiency and reliability of power systems by implementing the integration of diverse loads, renewable energy sources (RESs), and energy storage systems (ESSs). In this paper, the integration of multiple DC zero-carbon buildings (DC-ZCBs) is proposed to achieve the unbalanced voltage suppression of the bipolar DC microgrid (DCMG). The photovoltaic (PV) technology, loads, and DC electric springs (DC-ESs) are adopted as a unified entity to achieve the zero-carbon emission of the building. Firstly, a new configuration of PV and DC-ESs is introduced. The energy management of PV, ESS, and load are fully considered in this new configuration, which can reduce the capacity of the ESS. Subsequently, a distributed cooperative control strategy for DC-ESs based on the modulus voltage is presented, which is implemented with integration of the new configuration into the bipolar DCMG. The proposed approach addresses the issues of unbalanced voltage to improve the operating efficiency and power quality of the bipolar DCMG. The simulation is conducted in MATLAB/Simulink platform to confirm the effectiveness of the proposed approach.