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This paper presents a novel multi-objective model of active distribution network planning based on stochastic programming and uncertain random network (URN) theory. The planning model is proposed to find the final scheme with optimal alternative, location, size and operational strategy for the candidate distribution lines, transformer substations (TSs), distribution generations (DGs), static var compensators (SVCs) and on-load tap changers (OLTCs). Firstly, a scenario-based approach is developed to analyse the uncertainties in network system, such as the demand and intermittency of renewable sources. Since the impact of multiple uncertain factors on network cannot be ignored, a network frame is then modelled by uncertain and random weights of spanning tree (ST) instead of fixed value. In order to achieve the minimization of total cost, and further the selection of a minimum spanning tree (MST) with the uncertain and random weight, a 3-dimensional uncertain space is constructed based on the combination of the previous two targets. In addition, a second-order cone programming (SOCP) is applied to cope with the multi-objective, mixed-integer nonlinear nature of the proposed planning model. Simulation is performed on a modified Pacific Gas and Electric Company (PG&E) 69-bus distribution system, and the results demonstrate the effectiveness of the proposed model. [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]

Intermittent power generation of wind turbines and photovoltaic plants creates voltage disturbances in power distribution networks which may not be acceptable to the consumers. To control the deviations of the nodal voltages, it is necessary to use fast dynamic control of the reactive power in the distribution network. Implementation of the power electronic devices, such as Static Var Compensator (SVC), enables effective dynamic state as well as a static state of the nodal voltage control in the distribution network. This paper analyzed optimal sizing and location of SVC devices by using genetic algorithm, to improve nodal voltages profile in a distribution network with dispersed photovoltaic and wind power plants. Practical application of the developed methodology was tested on an example of a real distribution network. [ABSTRACT FROM AUTHOR]

• A two-stage robust planning model for the coupled system is proposed. • The uncertainty of renewable energy, load and traffic demand are jointly considered. • The interactions between transportation and active distribution system are explored. • A two-level method is used to solve the proposed robust optimization model. Widespread utilization of electric vehicles has incurred more uncertainties and led to profound interdependencies across the power grid and transportation network. This creates an urgent demand for uncertainty modelling tools. Under this context, this paper proposes a two-stage robust optimization model for expansion planning of coupled active distribution and transportation. This model identifies the best type, location and capacity for roads, distribution lines, distributed generations, static VAR compensators, on-load tap changers, energy storage systems and charging facilities. From a system-level perspective, the steady-state pattern of the traffic flow in a transportation network is characterized by the Wardrop user equilibrium. In the active distribution system, the conic relaxation-based branch flow equations are employed, and the operating status of related devices are formulated by the linearized convex constraints. Considering the interdependency between two coupled systems, a deterministic expansion planning model is first established. Furthermore, with multiple uncertainties considered, a two-stage robust optimization model is proposed to optimize the investment and operation strategies coordinately, and thus a robust optimal solution is achieved. A two-level algorithm based on a combination of the column-and-constraint generation and outer approximation technique is developed for solving the two-stage model. The results demonstrate that the robust approach can effectively reduce the operation cost of coupled systems under the worst-case scenarios. Moreover, a comparative analysis indicates that operation costs in both systems increase linearly as the uncertainty level of traffic demand increases, while increasing that of the power load does not show an obvious change in the traffic operation cost. [ABSTRACT FROM AUTHOR]

• A planning model of coupled transportation and active distribution system is proposed. • Scenario generation technique is extended to incorporate traffic demand. • Scenario-based user equilibrium constraint is derived and embedded into the model. • A three dimensional piecewise method is designed to relax the bivariate expressions. • The importance of considering interdependencies of the coupled system is verified. The widespread utilization of electric vehicles has inspired the emerging trend of coupled transportation and distribution system, which entails the systematic methodologies to model the new planning problems. This paper proposes a scenario-based comprehensive expansion planning model for a coupled transportation and active distribution system. With the aim of minimizing the investment and operation costs, this model determines the best alternatives, locations and sizes for candidate assets, including traffic roads, distribution lines, distribution generators, capacitor banks, static var compensators, voltage regulators, energy storage systems and charging facilities, as well as their operation strategies. First, a generated scenario method is extended to incorporate the uncertainty of the traffic flow demand. Based on multiple scenarios, the steady-state distribution of traffic flow is characterized by the Wardrop user equilibrium principle, and the corresponding equivalent constraints are derived and incorporated into the model. For active distribution system, we formulate the operation constraints for related infrastructures. Considering the interdependency between the two systems, an expansion planning model is proposed, which simultaneously optimizes the investment and operation strategies. Due to the nonlinear nature of the model, we have developed a three-dimensional piecewise linear approximation and applied second order cone relaxation to reformulate the model as a mixed-integer second order conic program; thus, the global optimal solution can be found in a reasonable time frame. Results on a test system reveal that the variations of both systems have some influences on each other, indicating the significance of considering the interdependencies between transportation and active distribution system. [ABSTRACT FROM AUTHOR]