Impacts of the Inclusion of Distributed Generation on Congestion of Distribution Networks and in the Islanding Operation Capability

The growing demand for electricity in the world has led to power systems having to constantly increase their generation capacity and expand their transmission and distribution systems. Consequently, distributed generation has positioned as a technology able to integrate generation close to consumption centers, freeing up capacity in the transport systems, which can be translated into a deferral of investments in network expansion. Therefore, this paper analyzes the impact of the inclusion of distributed generation in the congestion of a typical distribution network and evaluates the potential of providing the island operation capability ancillary service in a section of the system to identify the possible challenges and benefits that the development of this technical support service could have in typical Colombian distribution networks.

Weighted Islanding Detection for DC Microgrid Based on Random Forest Classification

At present, the main form of microgrid is AC grid. DC microgrids have received extensive attention and research with the rapid development of various DC power. The operation mode of the DC microgrid is divided into grid-connected operation and islanding operation. Islanding is formed after the circuit breaker tripped, which connects microgrid to large grid. Islanding operation can be divided into planned islanding and unplanned islanding. Unplanned islanding will cause certain harm to users and systems, so it is necessary to detect islanding accurately in the DC microgrid. This paper proposes an islanding detection method for DC microgrid based on random forest classification. Firstly, raw data is cleaned, extracted features and generated feature vector set. The extracted features include six islanding characteristic indexes, which consist of voltage, current, output active power and their first order backward difference on the DC bus side. Then, based on random forest classification, building the islanding detection model. Islanding detection model for DC microgrid can distinguish islanding event successfully and accurately. Based on weighted random forest classification, it can detect islanding event more accurately compared with decision tree classification when processing large amounts of data.

Optimization of Microgrid Battery Capacity using PSO with Considering Islanding Operation

Electrical is used for various activities in all sectors. Rapid increase of electricity demand recently, makes it necessary to have an even more efficient method for generating electricity. Renewable energy and the microgrid provides an integrated and alternative solution for electricity generation. In microgrid systems, energy storage devices are one of important aspects. Batteries are one kind of the energy storage technologies widely used in power system and hence, their suitable capacity must be determined in order to develop an effective system installation. In this research, sizing optimization of battery capacity is modeled as a minimization of microgrid battery capacity using the Particle Swarm Optimization/PSO algorithm with considering islanding operation of the system for effective battery installation. Results show that optimal battery capacity can be obtained and the developed computational model gives satisfactory results for the system under study. Keywords: Battery, microgrid, energy storage system, PSO algorithm