The integrity of distribution network state estimation is critically challenged by false data injection attacks, whose detection is often hampered by the numerical instability of underlying estimators. Such instability introduces artifacts that can mask an attack’s signature. This article presents a unified framework that achieves robust detection by decoupling these estimator artifacts from malicious data patterns. The framework’s core is a numerically stabilized forecasting aided state estimation employing a U-D factorization cubature Kalman filter (UD-CKF). By ensuring covariance positive-definiteness, it generates high-fidelity state estimates and, crucially, a statistically consistent innovation covariance matrix. This stable foundation enables a novel geometric inconsistency detector (GID). Instead of analyzing temporal patterns, the GID evaluates the geometric alignment between the observed innovation vectors and their expected statistical distribution defined by the UD-CKF. By monitoring the evolution of the innovation subspace, it effectively distinguishes the random orientation of noise from the persistent directional signature of a stealthy attack. This approach is validated on IEEE test feeders, demonstrating exceptional capability in identifying subtle FDIAs that remain undetected by magnitude-based or purely temporal methods.

Managing energy usage flexibility has been identified as an effective way to coordinate Internet of Things (IoT) technologies and transition to a low-carbon future. However, the various models of these flexibilities may not be fully understood, and some may even be characterized by closed-box neural networks, such as those used for electric vehicles (EVs) charging. In this article, we propose a novel framework called augmented shadow-price deep reinforcement learning (ASP-DRL) for the online, distributed management of IoT under multiple sources of uncertainties, including renewable energy sources (RESs), wholesale electricity prices, and EV behavior patterns. To address these challenges, the proposed framework combines scheduling mechanisms for neural networks and optimization models to maximize total social welfare. Within the ASP-DRL framework, the policy network adaptively learns about system uncertainties and delivers actions to different distributed entities, either to form augmented objective functions for optimization models or to guide neural networks. We also present a distribution correction algorithm that enhances the vanilla soft Actor-Critic method with attention-based maximal corrective feedback, resulting in faster convergence and better performance. Our numerical studies demonstrate the superiority of the proposed ASP-DRL framework compared to conventional deep reinforcement learning (DRL) and optimization-based approaches.

Regulating the proper carbon-aware intervention policy is one of the keys to emission alleviation in the distribution network, whose basis lies in effectively attributing the emission responsibility using emission factors. This paper establishes the distribution locational marginal emission (DLME) to calculate the marginal change of emission from the marginal change of both active and reactive load demand for incentivizing carbon alleviation. It first formulates the day-head distribution network scheduling model based on the second-order cone program (SOCP). The emission propagation and responsibility are analyzed from demand to supply to system emission. Considering the complex and implicit mapping of the SOCP-based scheduling model, the implicit theorem is leveraged to exploit the optimal condition of SOCP. The corresponding SOCP-based implicit derivation approach is proposed to calculate the DLMEs effectively in a model-based way. Comprehensive numerical studies are conducted to verify the superiority of the proposed method by comparing its calculation efficacy to the conventional marginal estimation approach, assessing its effectiveness in carbon alleviation with comparison to the average emission factors, and evaluating its carbon alleviation ability of reactive DLME.

A phasor measurement unit (PMU) serves as a superior tool to monitor the dynamics of the power system, but its high cost remains a practical concern that requires the optimal placement of the PMU (OPP). Traditionally, researchers relied on model-based approaches to analyze this problem. However, these methods not only suffer from inevitable parameter uncertainties but can also be computationally expensive for complicated power system dynamic models. Faced with these issues, this article proposes a data-driven OPP approach utilizing an augmented Koopman operator. This operator lifts the original nonlinear state space to a high-dimensional linear Koopman space in a data-driven manner, which fully eliminates the model discrepancy while achieving high computing efficiency. Theoretically, we prove that the observability matrix in the augmented Koopman canonical coordinates preserves the whole dynamic evolution of both the system model and its associated measurement model. Finally, we propose a modified genetic algorithm to solve the established OPP problem, which is enhanced to further accelerate the search speed. The simulation results reveal the excellent performance of our proposed method.

Power distribution networks are evolving toward a modernized cyber-physical system that is vulnerable to mounting cybersecurity threats brought by false data injection attacks (FDIAs). To address the issue, this article innovatively proposes a multiarea probabilistic forecasting-aided interval state estimation (MPF-ISE) framework for FDIA identification in power distribution networks. The framework devises a novel probabilistic forecasting (PF) approach to achieve interval pseudo measurement modeling. A nonlinear programming-based training algorithm is formulated to minimize the interval width of forecasting error quantiles, which are analytically approximated by Cornish-Fisher expansion. Next, real-time interval measurements are constructed based on the unknown-but-bounded theory. By implementing interval measurement conversion, the local PF-ISE model is converted into rectangular coordinate forms and iteratively solved using a modified Krawczyk operator. Then, the local model is extended to a multiarea form and solved by considering the information exchange between adjacent subareas. Finally, the solution of MPF-ISE, which takes into account measurement uncertainties and line parameter variations, is regarded as the normal operating level, thus forming the proposed FDIA identification scheme. Case studies on the modified IEEE 123-node test feeder demonstrate the superiority of the proposed PF and the effectiveness of MPF-ISE in identifying FDIAs compared with existing ones.

The state estimation method is troubled by heavy computational tasks and poor estimation tracking capability for the large-scale active distribution network. Given the aforementioned difficulty, in this article, we proposed a novel multiarea forecasting-aided state estimation (FASE) strategy to perceive the state of the system effectively. The proposed strategy begins with the implementation of an improved multiarea FASE model. The processing of multisource measurement data, such as microphasor measurement units and supervisory control and data acquisition, and equivalent load-based information interaction reliably complete the FASE of multiareas. Especially, a third degree dimensionality reduction square root cubature Kalman filter (SR-CKF) algorithm is designed for local FASE model considering the influence of large-scale distribution networks data on the numerical stability of the estimator. The case study shows the advantages of the proposed strategy in estimation accuracy, efficiency, and numerical stability compared with the existing ones.

Widespread advanced metering infrastructure and wide-area monitoring systems generate a significant amount of electricity load consumption data, which can facilitate eliciting end users’ temperature flexibility for demand response programs. However, the direct delivery of users’ load profiles is a threat to users’ privacy. So this paper proposes a privacy-preserving hybrid cloud framework for TCL-based demand response programs, composed of user private clouds and aggregation cloud. User clouds store users’ load profiles and elicit temperature flexibility by the proposed stable temperature-related regression model. In the aggregation cloud, this paper proposes the slope-priority flexibility aggregation method for the mean-variance analysis of aggregate flexibility and the XGBoost-accelerated disaggregation model for real-time selecting users based on users’ fitting coefficients. Hybrid cloud achieves privacy-preserving by separating flexibility eliciting models and aggregation/disaggregation methods into user private clouds and aggregation cloud. Numerical experiments verify that: 1) in user clouds, the stable regression model achieves less predict errors; 2) in aggregation cloud, the slope-priority method can achieve higher aggregate flexibility, and XGBoost-accelerated disaggregating reduces the solving time by nearly three orders of magnitude.

A grand challenge for the state estimation (SE) method in large-scale distribution networks lies in how to deal with the increasing computational tasks. This article addresses the issue and proposes a novel multiarea architecture for the unbalanced distribution network SE method. The first step of the method is to introduce an innovative multiarea state estimation (MASE) model using microphasor measurement units (μPMU) mixed with conventional supervisory control and data acquisition (SCADA) systems, with both the coordinate tensions and synchronization issues considered. The proposed model contains a SCADA measurement delay estimator and a MASE algorithm. Then, the hybrid state estimation (HSE) model is solved in a distributed way. In each subarea, the HSE problem is solved locally with minimal data exchanges among neighbor subareas. Case studies show the accuracy and efficiency enhancements obtainable of the proposed MASE method with respect to existing ones.

The increasing penetration of photovoltaic (PV) generators has led to a reduction in the effectiveness of existing strategies for restoring the power distribution network. This article proposes a dynamic robust restoration (DRR) framework for the recovery of outage power considering uncertain PV outputs and demands. This framework is presented in two subsequent steps. In the first step, optimal decisions regarding the network configurations are generated. The second step then computes the modified dynamic Distflow equations and constraints under consideration of the worst operating conditions over the associated uncertainty sets with the aim of maximizing the recovery of outage power. The DRR model is formulated as a bilevel mixed-integer linear programming problem. A decomposition algorithm in a master-sub structure is used to solve the resulting system. The results of case studies show that the proposed DRR model yields obvious advantages over the existing deterministic dynamic restoration model in terms of robustness against system uncertainties.

In this paper, an adaptive active power optimal allocation strategy for minimizing power loss during power transmission in islanded microgrids is proposed. A secondary regulated variable is added to the P-f droop control as a frequency offset to regulate power output reasonably among multiple distributed generation (DG) units. The rate of change of the total power loss during power transmission is calculated. The frequency offset is adaptively generated in turn among multiple DG units according to the rate of change of power loss. Thus the power flowing through different transmission lines is optimized and the power loss is minimized. Simulation results validate the effectiveness of the proposed strategy.

Distribution networks with high penetration of distributed generation yield complicated and uncertain power flow, which makes most existing faulted line identification methods not adaptable for industrial applications. Driven by this motivation, a novel single-phase-to-ground (SPTG) faulted line identification method is proposed based on hybrid state estimator (HSE). The first step of the method is to present an HSE for power distribution networks using power flow measurements mixed phasor measurement units. Then, an SPTG fault on a power line is treated as an event that suddenly increases one virtual bus in the monitored network, so as to form the extended bus admittance matrix and augmented HSE based on the specific network topology. In this way, the faulted line identification could be obtained by computing parallel estimated results transversally. Robustness and effectiveness of the proposed HSE and the HSE-based SPTG faulted line identification method are validated by means of a cyber-physical system (a cosimulation platform), where two typical three-phase power distribution networks are considered to simulate with its hybrid measurement system.

Advanced digital filter is important for grid voltage synchronization, harmonic information extraction, and converter control. The complex variable filter (CVF) such as the reduced order generalized integrator is one of the most prevalent filters. However, it is proven in this paper that the real coefficient CVF achieves performance identical to the real variable filter such as the second-order generalized integrator. It implies that the advantages of CVF are not fully utilized. Therefore, in this paper, the real coefficient is extended to a complex one leading to a complex coefficient CVF (CC-CVF), and the filter performance of CVF for extracting the harmonics is significantly improved. Furthermore, linear quadratic regulation is employed to design the complex coefficients to achieve a better dynamic performance. Finally, the CC-CVF is enhanced with frequency estimation capability. The proposed CC-CVF is verified by extensive simulations and experiments.

The grid synchronization system, which plays an important role in the control of grid-connected power converters, often needs to manipulate the grid voltage to extract the fundamental and harmonic sequence components. Generally a prefilter can achieve this function. In this paper, a systematic and general approach to design the multiple block based prefilter is introduced. First a complex variable discrete resonantor is adopted in the prefilter. Furthermore, the multiple coefficients of the prefilter are tuned by means of minimum energy method with variance constraint from control perspective. The proposed approach is described as an optimal solution in form of linear matrix inequality (LMI). Moreover, the approach is allowed to customize the settling time of the prefilter by designer. In addition, the prefilter yielded from the approach also performs good dynamic performance such as the fast settling time and smooth transient waveforms. The effectiveness and superiority of the proposed approach is verified by the numerical results.

To ensure stable and optimal operation of DC microgrid, a three-level based hierarchical control strategy is proposed which consists of primary, secondary and tertiary controls. Usually, the primary control is performed locally without communication, the higher levels, however, can be either centralized or distributed. Considering the risks brought by centralized control, such as surviving central node or link failures, this paper proposes a distributed secondary control strategy for DC microgrid which is designed by Discrete Average Consensus Algorithm (DACA). In this paper, the controller embedded the distributed strategy will communicate with their neighbors through Low Bandwidth Communication to obtain average voltage of the whole system or power flow of grid-connected converter. Through the proposed novel strategy, the secondary control objectives such as reference power control, equal load sharing, voltage regulation will be realized in distributed means. Furthermore, the influence of communication delay to the strategy is studied at length, and the effectiveness and robustness of the proposed strategy are verified through detailed simulations.