Configuring energy storage systems (ESSs) in distribution networks is an effective way to alleviate issues induced by intermittent distributed generation such as transformer overloading and line congestion. However, flexibility has not been fully taken into account when placing ESSs. This paper proposes a novel ESS placement method for flexible interconnected distribution networks considering flexibility constraints. An ESS siting and sizing model is formulated aiming to minimize the life-cycle cost of ESSs along with the annual network loss cost, electricity purchasing cost from the upper-level power grid, photovoltaic (PV) curtailment cost, and ESS scheduling cost while fulfilling various security constraints. Flexible ramp-up/-down constraints of the system are added to improve the ability to adapt to random changes in both power supply and demand sides, while a fluctuation rate of net load constraints is also added for each bus to reduce the net load fluctuation. The nonconvex model is then converted into a second-order cone programming formulation, which can be solved in an efficient manner. The proposed method is evaluated on a modified 33-bus flexible distribution network. The simulation results show that better flexibility can be achieved with slightly increased ESS investment costs. However, a large ESS capacity is needed to reduce the net load fluctuation to low levels, especially when the PV capacity is large.

Multi-port soft open points (SOPs) are effective devices for alleviating issues such as voltage violation and transformer overloading in distribution networks caused by the high penetration of distributed energy resources. This paper proposes a coordinated voltage and power control method for flexible distribution networks based on a linear quadratic regulator (LQR). First, the principle of coordinated voltage and power control is analyzed based on SOPs’ control strategies and a linear power flow model. Then, a discrete-time state-space model is constructed for flexible distribution networks with multi-port SOPs, using the voltage magnitude deviations at the AC side of all PQ-controlled voltage source converters (VSCs) and the loading rate deviations of the transformers corresponding to all PQ-controlled VSCs as state variables. An LQR-based optimal control model is then established, aiming to simultaneously minimize deviations of voltage magnitudes and transformer loading rates from their reference values, which correspond to the V_dc-controlled VSC. The optimal state feedback law is obtained by solving the discrete-time algebraic Riccati equation. The proposed method has been evaluated on two typical flexible distribution networks, and the simulation results demonstrate the effectiveness of the proposed control method in improving voltage profiles and alleviating transformer overloading conditions using local measurements and very limited communication. In specific situations, the imbalance of voltages and transformer loading rates among the interconnected feeders can be reduced by 40%.

Accurate fault localization is critical for ensuring reliable power supply in active distribution networks, yet conventional state estimation (SE)-based methods fail to differentiate authentic fault responses from measurement distortions due to uncertainties in fault parameters. To overcome this limitation, a robust three-phase SE-driven fault localization methodology is proposed. First, a measurement transformation-based SE model is built for fault conditions, leveraging real-time voltage phasor measurements and pseudo-measurements derived from pre-fault SE results. Then, a robust fault SE model is built using the quadratic-constant-based generalized maximum likelihood estimation, solved through the iteratively reweighted least squares algorithm that postpones phasor measurement weight updates until after initial iterations to prevent residual contamination. Furthermore, a fault localization algorithm is proposed through the systematic traversal of candidate buses, where each potential fault localization is assessed by performing robust fault SE with the fault current injected into this bus. The matching index is designed, accounting for the weight disparity of different types of measurements and measurement placement. Extensive simulations on a 33-bus unbalanced distribution network validate the method’s effectiveness under various measurement noise levels, fault resistances and incorrect data severity. The approach maintains comparable accuracy to conventional SE under normal operating conditions, while it exhibits superior robustness against measurement anomalies and effectively preserves fault localization reliability when confronted with incorrect data.

As the final stage of electrical energy delivery, distribution networks play a vital role in ensuring reliable power supply to end users. In regions with limited distribution automation, reliance on operator experience for fault handling often prolongs outage durations, undermining energy sustainability through increased economic losses and carbon-intensive backup generation. Remote-controlled switches (RCSs), as fundamental components of distribution automation, enable remote operation, rapid fault isolation, and load transfer, thereby significantly enhancing system reliability. In the process of intelligent distribution network upgrading, this study targets scenarios with sufficient line capacity and constructs a reliability-oriented analytical model for optimal RCS allocation by traversing all possible faulted lines. The resulting model is essentially a mixed-integer linear programming formulation. To address bilinearities, the McCormick envelope method is applied. Multi-binary products are decomposed into bilinear terms using intermediate variables, which are then linearized in a stepwise manner. Consequently, the model is transformed into a computationally efficient mixed-integer linear programming problem. Finally, the proposed method is validated on a 53-node and a 33-bus test system, with an approximately 30 to 40 times speedup compared to an existing mixed-integer nonlinear programming formulation. By minimizing outage durations, this approach strengthens energy sustainability through reduced socioeconomic disruption, lower emissions from backup generation, and enhanced support for renewable energy integration.