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Highlights • An analytical framework for quantifying the flexibility of ADNs was proposed. • Flexibility of network transmission was analysed with the limits of physical network. • The benefits of controllable resources for flexibility improvement were explored. Abstract With the integration of high shares of distributed generators (DGs), it is increasingly difficult to cope with the various uncertainties and puts forward higher requirements for the operational flexibility in active distribution networks (ADNs). Controllability of ADNs has been significantly improved by the application of energy storage and power electronic devices. However, due to the difficulties in effective coordination of various controllable resources, the controllability cannot be fully translated into the system flexibility. In this paper, an analytical framework for quantifying the flexibility of ADNs is proposed, including the quantification of node flexibility, the matching of system flexibility, and the flexibility of network transmission. The proposed framework provides a novel perspective of flexibility to reinterpret operation issues of distribution networks. As the power imbalance and voltage deviation are even more severe caused by the high shares of DG integration, the indexes of flexibility from magnitude, frequency and intensity dimensions are deteriorated in ADNs operation. Through the spatial and temporal regulation of power flow, various controllable devices, such as soft open points (SOPs) and energy storage system (ESS), can effectively mitigate the power imbalance and voltage deviation to improve the indexes of system flexibility. Thus, under the unified analytical framework, the potential benefits of system controllability are fully utilized to provide effective countermeasures for the flexible operation of ADNs. Finally, case studies are performed on the modified IEEE 33-node system to quantify the operational flexibility of ADNs and verify the flexibility enhancement brought by controllable resources. [ABSTRACT FROM AUTHOR]

There is an increasing awareness of resilience for active distribution networks (ADNs) as extreme fault conditions pose threats to the reliable operation of power supply. Islanding operation of ADNs with distributed generators (DGs) and energy storage system (ESS) can significantly serve the critical electricity demands and improve the power supply reliability. Considering the characteristics of DG, ESS and load, a time-series islanding partition model of ADNs is established based on soft open point (SOP). The proposed model exploits the benefits of operational flexibility provided by SOP to improve the level of load recovery. It further takes into account the coordination of the control strategies among regulation devices as well as the failure duration in island partition, while satisfying a variety of operational constraints. Specifically, a mixed-integer nonlinear programming (MINLP) is employed to solve the proposed model effectively. Finally, case studies on IEEE 33-node and IEEE 123-node test feeders verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

With the integration of high shares of distributed generators (DGs), both volatile DGs and demand-side resources make it increasingly difficult to cope with the various uncertainties and put forward a higher requirement for the operational flexibility in active distribution networks (ADNs). The highly integrated distribution-level power electronic devices represented by soft open point (SOP) significantly benefit the operation of ADNs. Due to the accurate and rapid power flow control provided by SOP, various flexible resources can be coordinated in the spatial and temporal aspects, which effectively increases the DG penetration of ADNs. In this paper, the multi-dimensional characteristics of distribution system flexibility are analysed and the maximum DG hosting capacity is adopted as the index to evaluate the flexibility brought by SOP. The flexibility evaluation model is further proposed to quantify the benefits of SOP-based flexible resources, such as conventional SOP, multi-terminal SOP and SOP with energy storage. Then, the original non-convex nonlinear model is converted into an effectively solved second-order cone programming (SOCP) model using convex relaxation. To tighten the deviation of convex relaxation to the predefined accuracy, a difference-of-convex programming (DCP)-based approach is developed to solve the proposed model. Finally, case studies are performed on the modified IEEE 33-node system to verify the effectiveness and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]