Available Delivery Capability of General Distribution Networks With Renewables: Formulations and Solutions

Abstract

The widespread use of distributed generators (DGs) in utility distribution feeders brings about several challenges to the operation, planning, and design of general distribution networks. In this paper, the task of accurate determination of available delivery capability (ADC) subject to thermal limits, voltage limits, and voltage stability limit is formulated. A rigorous numerical method to calculate the ADC of large-scale distribution networks with renewables is presented. This numerical method computes three critical points which are the saddle-node bifurcation point or structure-induced bifurcation point, voltage violation point, and thermal-limit violation point. To gain the speed and robustness for the proposed method, the continuation method is integrated into the proposed numerical method. For illustrative purposes, the proposed method is applied to the IEEE 14-bus test feeder and the IEEE 8500-node test system.

Applying Polynomial Chaos Expansion to Assess Probabilistic Available Delivery Capability for Distribution Networks With Renewables

“…Generate an experimental design of size M C . This process can be implemented as the following steps • a) Generation M C samples ξ C = (ξ (1) , ξ (2) , ..., ξ (M C ) ) in standard space by the Latin hypercube sampling (LHS). • b) Transform ξ C into physical space by the inverse Nataf transformation u C = T −1 N ataf • T −1 4 (ξ C ); • c) Evaluate u C by deterministic simulation tool, say CDFLOW tool [46], to obtain the accurate responses y C = (y (1) , y (2) , ..., y (M C ) ); The set of sample-response pairs (ξ C , y C ) can then be used to build the PC expansions of (14).…”

Toward coordinated look‐ahead reactive power optimisation for distribution networks with minimal control

“…The operational constraints are the load margin constraints, voltage constraint, and control physical constraints. ADCM is a direct measure of the capability of a distribution network to delivery power from the source area (such as a collection of nodes which renewable energies are connected to) to the sink area (such as a collection of loads) without voltage violations and voltage collapse [12]. Under slowly varying loading and distributed generating conditions, a parameter λ can be introduced into power-flow equations to simulate power system steady-state stationary behaviours with respect to a variety of power injection variations including penetration of DGs.…”

“…To enhance the network capability of withstanding a certain range of variations in loads and DG outputs, we propose to use the metric of ADC margin (ADCM), which is the capability of a distribution network to deliver power from the locations of renewable resources to loads without voltage violation and voltage collapse [12]. It is important to have sufficient hourly ADCM (alternatively, load margin) to fully support the penetration of DGs [13].…”

Statistical Inference for Visualization of Large Utility Power Distribution Systems

“…Studies [7,8,[10][11][12][13][14][15]17,18,24,25,27,29] show the application of this approach to validate proposed strategies or devices. In summary, the following are examples of typical applications for snapshot analysis and validation:…”