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This study deals with the voltage stability constrained optimal power flow (VSC-OPF) problem considering the wind power generation uncertainty. The main feature of the proposed model is to handle the uncertainty of multiple wind farms (WFs) in a way that for a given worsening of total cost, maximum tolerable uncertainty of wind power generation is achieved for all WFs. This maximum uncertainty is determined in a way that a required loading margin (LM), is preserved. It is worth noting that LM is the most important measure of voltage stability which reflects the distance from the current operating point to the voltage collapse point. For this aim, information gap decision theory (IGDT) is utilised to handle the uncertainty of wind power generation. The proposed model is implemented on IEEE 39-bus standard test system. In order to evaluate the effectiveness of the proposed VSC-OPF model for uncertainty handling of multiple WFs, the results obtained by IGDT technique are compared with Monte Carlo simulations and scenario-based approach. The simulation results imply that the uncertainty radius and the desired LM are inversely related, such that for a given tolerable increase of cost, the radius of uncertainty decreases by increasing the desired LM. Δ c critical/opportunistic value of objective function to be maintained at presence of uncertainty κ 1 b /κ 2 b coefficients for modelling the lower/upper limits of WFs reactive power outputs Λ w b rated active power of WF connected to bus b

The main goal of distribution network operator is to establish a balance between supply and demand at the lowest cost while considering the technical constraints. Nowadays, distribution network operators exploit various types of flexibilities to minimise operational costs. However, each flexibility resource has its own technical and economic characteristics. This paper proposes a day‐ahead energy dispatch model which allows the distribution network operator to minimise the energy procurement costs on an hourly basis. The developed model considers various flexibility resources such as renewable energy sources, energy storage systems, demand response, optimal distribution system reconfiguration, and on‐load tap changers optimal settings. The proposed model is formulated as a convex mixed‐integer second‐order conic programming model. It is implemented on the IEEE standard 33‐bus and 70‐bus radial systems to demonstrate its capabilities. The obtained numerical results substantiate the role of distributed energy resources in energy procurement cost reduction, while the impact of distribution system reconfiguration and on‐load tap changers on voltage profile improvement.

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