Jiayong Li

The proliferation of distributed energy resources (DERs) introduces significant uncertainties into distribution networks (DNs), threatening operational security. Concurrently, low-voltage DNs (LVDNs) interconnected via voltage source converters (VSCs) can form microgrid clusters (MCs), effectively mitigating voltage violations and load imbalances. To address the propagation of these multi-source uncertainties, this paper proposes a novel hierarchically coordinated operation framework for the DN and MCs. Within this framework, the MC layer manages localized uncertainty absorption and energy management, while the DN layer globally accommodates residual uncertainties and safeguards the overall system security based on the outcomes of MCs. Leveraging an online battery energy storage system (BESS) response strategy, a two-stage distributionally robust chance-constrained optimization (DRCO) model is established to co-optimize the day-ahead operational baselines and real-time mitigation factors. Chance constraints are formulated to jointly ensure BESS operational feasibility and network security, balancing robustness with mitigation efficiency. A computationally efficient solution methodology is further developed, which integrates chance constraint transformation with strong duality theory to reformulate the model into a tractable form. Simulations on a modified IEEE 123-bus DN with 8 MCs validate the effectiveness and superiority of the proposed approach in enhancing security, improving economics and mitigating uncertainty impacts.

With the growing integration of renewable energy sources into power grids, the risks of oscillation caused by interactions between grid-tied inverters and the grids are becoming increasingly prominent. Although existing studies have made significant progress in inverter modeling and oscillatory stability analysis, most of them do not sufficiently consider complex mirror frequency coupling effects (MFCE) under unbalanced operating conditions, leading to unreliable models and erroneous stability analysis results. To address this inadequacy, this work develops a novel sequence impedance modeling scheme that can be widely applied to unbalanced operating conditions. In particular, taking a representative type of grid-forming inverter for instance, i.e., droop-controlled inverter (DCI), a single-input single-output sequence impedance modeling method based on harmonic linearization (HL) is proposed to comprehensively model both a given DCI and the connected grid. By accounting for multifrequency interactions within the DCI, this method captures MFCE and unbalanced factors, leading to a more accurate impedance model. Further, the dominant factors influencing system stability are identified with a combination of normalized sensitivity analysis and proportional weighting. Finally, the detailed impacts of these dominant factors on system stability margin under three typical unbalanced operating conditions are analyzed through the Bode criterion. The effectiveness and reliability of the whole scheme proposed in this work are validated on the constructed grid-connected droop-controlled experimental platform.

This article proposes a novel N–1 interval optimal power flow (NIOPF) scheme that considers N–1 contingencies and interval uncertainties in renewable generation. Based on the scenario-equivalent decoupling method (SEDM), the NIOPF addresses the problems associated with existing methods, including constraint violations and low solution efficiency. First, the NIOPF model is formulated by integrating security constraints involving interval variables under both N–0 normal and N–1 contingency scenarios, along with scenario transition constraints. Second, a credible domain reduction method (CDRM) based on the Minkowski difference is proposed to handle interval constraints, converting them into tractable deterministic constraints that guarantee the system operates within safe limits across all uncertain scenarios. Then, the SEDM represents numerous N–1 scenarios equivalently as two positive and negative deviation scenarios, effectively reducing the dimensionality of N–1 scenarios. Next, a two-stage optimization model that integrates the N–0 normal operating scenario with the two equivalent deviation scenarios is constructed and solved iteratively to improve computational efficiency. Finally, case studies on the modified IEEE 30-bus system verify that the proposed SEDM ensures constraint satisfaction under uncertainties while improving computational efficiency. Further tests on the modified IEEE 118-bus and PEGASE 1354-bus systems confirm its engineering applicability.

The widespread integration of distributed energy resources (DERs) introduces unprecedented uncertainties and complexities in distribution system planning. To enhance local DER accommodation, low-voltage flexible DC interconnection technology has advanced rapidly, enabling capacity sharing and mutual energy support among distribution areas (DAs). This paper proposes a novel planning methodology to optimize flexible DC interconnection device placement. First, a novel initial solution filtering approach is devised based on comprehensive evaluation metrics considering influencing factors like line distances, energy surplus and transformer loading rate to preliminarily screen feasible DA interconnection pairs. Subsequently, a two-stage chance-constrained stochastic programming (TCCSP) model is established for placing DC interconnection devices, with voltage limits, transformer loading limits and DER utilization levels formulated as chance constraints. To solve the TCCSP efficiently, an innovative sampling-decomposition algorithm is developed by integrating sampling average approximation (SAA) method with column-and-constraint generation (CCG) algorithm, converting the chance constraints into tractable deterministic equivalents and decomposing the problem into a master problem with multiple scenario-specific subproblems. Finally, numerical tests on the IEEE 123-bus distribution network validate the high efficiency and effectiveness of the proposed planning method.

Existing fault location approaches necessitate the deployment of micro-phasor measurement units ( $\boldsymbol {\mu }$ PMUs) at all terminal buses to achieve precise fault location on any line, leading to high costs and limited applicability in large, unbalanced active distribution networks (ADNs) with numerous branches. To address this issue, this paper proposes a cost-effective fault location method based on a sparse hybrid measurement scheme (SHMS), which replaces $\boldsymbol {\mu }$ PMUs on branches with conventional measuring devices (CMDs). First, a sparse fault current extraction method is proposed, and the conditions for the uniqueness of the sparse fault current solution are provided. Building on these results, a low-cost SHMS is proposed. Finally, a two-stage fault location method is presented to precisely locate faults on any line within ADNs. For the unbalanced IEEE 123-bus test system with numerous branches, the proposed method requires only $13~\boldsymbol {\mu }$ PMUs and 32 CMDs—compared to the conventional requirement of $45~\boldsymbol {\mu }$ PMUs—to locate faults on any line, thereby significantly reducing measurement costs. Simulation results indicate that, under diverse fault conditions, the fault location success rate reaches 98.30% for three-phase main lines and 96.16% for branches.

To overcome the problem of low computing efficiency in interval AC power flow (IACPF) analysis under uncertainty in renewable energy penetrated power systems, this paper proposes a rapid and less conservative interval power flow (IPF) approach based on a scenario reflection method. First, a linearized interval power flow (LIPF) model is developed by depicting bus injection intervals comprising known nodal variables to characterize uncertainties. Then, a rapid scenario reflection (RSR) method is proposed to accelerate the model solution. Unlike traditional optimization approaches, this method reduces computational burden by conducting structural analysis and scenario reflection of the model. Furthermore, considering variables’ statistical information, the RSR method is enhanced by incorporating a distributional interval algorithm (DIA) to reduce the conservativeness of interval results. Moreover, given the potential correlation of renewable power generation and load demands, a correlation-aware DIA based on Copula theory and principal component analysis (PCA) is proposed to enhance the accuracy by leveraging the probability distribution information of uncertain scenarios. Finally, comprehensive numerical experiments on various transmission and distribution test systems validate the wide applicability of the proposed methods, showing significant improvements in both computational efficiency and result tightness, while maintaining high accuracy compared to existing IACPF approaches.

This paper proposes an optimal power flow method considering interval uncertainties (OPFIU) to enhance the security and economy of power systems with high renewable energy penetration. The OPFIU incorporates bi-level interval power flow (BIPF) equations, security and operation constraints, and active power balance across two extreme renewable output scenarios. Firstly, a dynamic-balance optimization scenario method (DOSM) is proposed to solve the BIPF within the OPFIU. The BIPF involves multiple generators in dynamically balancing the system’s active power, thus reducing the conservatism prevalent in existing interval power flow methods. Next, based on the BIPF results, an adaptive security limits definition method (ASLDM) is presented to convert the OPFIU model into a deterministic optimal power flow model, making it more tractable for solution. The ASLDM not only improves the solution efficiency and convergence of existing interval optimization methods, but also provides enhanced reliability in maintaining transmission power within operational limits across a broad spectrum of uncertainty scenarios. Moreover, the ASLDM, unlike robust optimization methods that require convexification of nonlinear AC power flow equations, partially avoids potential issues such as accuracy degradation or solution infeasibility resulting from model simplifications. The Simulation results, compared with robust optimization and chance-constrained programming methods, demonstrate that the proposed ASLDM improves both solution efficiency and convergence in solving the OPFIU problem.

Existing studies on identifying outliers in wind speed-power datasets are often challenged by the complicated and irregular distributions of outliers, especially those being densely stacked yet staying close to normal data. This could degrade their identification reliability and robustness in practice. To address this defect, this paper develops a three-stage composite outlier identification method by systematically integrating three complementary techniques, i.e., physical rule-based preprocessing, regression learning-enabled detection, and mathematical morphology-based refinement. Firstly, the raw wind speed-power data are preprocessed via a set of simple yet efficient physical rules to filter out some outliers obviously going against the physical operating laws of practical wind turbines. Secondly, a robust wind speed-power regression learning model is built upon the random sample consensus algorithm. This model is able to reliably detect most outliers with the help of an adaptive threshold automatically set by the interquartile range method. Thirdly, by representing the wind speed-power data distribution with a two-dimensional image, mathematical morphology operations are applied to perform refined outlier identification from a data distribution perspective. This technique can identify outliers that are not effectively detected in the first two stages, including those densely stacked ones near normal data points. By integrating the above three techniques, the whole method is capable of identifying various types of outliers in a reliable and adaptive manner. Numerical test results with wind power datasets acquired from distinct wind turbines in practice and from simulation environments extensively demonstrate the superiority of the proposed method as well as its potential in enhancing wind power prediction.

With the growing adoption of renewable energy, conventional synchronous generators (CSGs) are gradually substituted by the renewable energy generation units, resulting in a significant decline in the inertia level and anti-disturbance capability of power systems. Consequently, the frequency security problem becomes increasingly prominent, which in turn hinders the effective accommodation of renewable energy. Hence, this paper proposes a novel frequency dynamics-constrained evaluation method of renewable energy hosting capacity (REHC) in power systems to provide insightful guidance for the healthy development of renewable energy. Firstly, we analyze the dynamic response mechanism in frequency regulation using various resources including CSG, energy storage and renewable energy. Subsequently, the frequency security algebraic constraints are derived based on the first-order swing equation considering the heterogeneity of multi-resource response characteristics and load-damping effect. Then, we formulate the REHC evaluation model with frequency security constraints considering both economic and technical aspects. To reduce the computation complexity and guarantee high-level accuracy, the piecewise McCormick envelop method is employed to relax frequency security constraints as linear equations, hence converting the original problem into a mixed-integer linear programming (MILP) problem. Eventually, the proposed REHC evaluation method is tested on the modified IEEE 39-bus system, and its effectiveness is verified by the numerical results.