Stochastic assessment of distributed generation hosting capacity and energy efficiency in active distribution networks

• Published in: IET generation, transmission & distribution transmission & distribution, 2017-12, Vol.11 (18), p.4617-4625
• Main Authors: Quijano, Darwin Alexis, Wang, Jianhui, Sarker, Mushfiqur R, Padilha-Feltrin, Antonio
• Format: Article
• Language: English
• Subjects: active distribution network; active network management; ANM approach; compensation; DG generation curtailment; distributed generation hosting capacity; distributed power generation; distribution networks; energy conservation; energy efficiency; energy loss minimization; energy resource; IEEE test feeder; linearised optimal power flow; load flow; multiperiod multiobjective optimisation algorithm; on load tap changers; on‐load tap changer; power consumption; power system management; reactive power; reactive power compensation; Research Article; stochastic assessment; stochastic processes; stochastic programming; two‐stage stochastic programming formulation; voltage regulator; voltage regulators; voltage‐dependent load consumption
• ISSN: 1751-8687; 1751-8695; 1751-8695
• DOI: 10.1049/iet-gtd.2017.0557

Active network management (ANM) aims to increase the capacity of variable distributed generation (DG), which can be connected to existing distribution networks. In this study, it is proposed to simultaneously consider the efficient use of energy resources when high shares of DG are procured through the ANM approach. To that end, a multi-period and multiobjective optimisation algorithm, based on the linearised optimal power flow, is formulated. The algorithm seeks to maximise the installed capacity of DG while minimising the energy losses and consumption of voltage-dependent loads. The objectives are optimised considering the coordinated operation of voltage regulators and on-load tap changers, and the management of DG generation curtailment and reactive power compensation from DG. Additionally, the effects of load and generation uncertainties are addressed through a two-stage stochastic programming formulation of the multiobjective problem. The result is a set of non-inferior solutions, which allows exploring the degree of conflict among the objectives. The proposed approach was tested on two IEEE test feeders and the solutions show a significant improvement in the system's energy efficiency with a low impact on the amount of connected DG.