Stability of microgrids and weak grids with high penetration of variable renewable energy

Autonomous microgrids and weak grids with high penetrations of variable renewable energy (VRE) generation tend to share several common characteristics: i) low synchronous inertia, ii) sensitivity to active power imbalances, and iii) low system strength (as defined by the nodal short circuit ratio). As a result of these characteristics, there is a greater risk of system instability relative to larger grids, especially as the share of VRE is increased. This thesis focuses on the development of techniques and strategies to assess and improve the stability of microgrids and weak grids. In the first part of this thesis, the small-signal stability of inertia-less converter dominated microgrids is analysed, wherein a load flow based method for small-signal model initialisation is proposed and used to examine the effects of topology and network parameters on the stability of the microgrid. The use of a back-to-back dc link to interconnect neighbouring microgrids and provide dynamic frequency support is then proposed to improve frequency stability by helping to alleviate active power imbalances. In the third part of this thesis, a new technique to determine the optimal sizing of smoothing batteries in microgrids is proposed. The technique is based on the temporal variability of the solar irradiance at the specific site location in order to maximise PV penetration without causing grid instability. A technical framework for integrating solar PV plants into weak grids is then proposed, addressing the weaknesses in conventional Grid Codes that fail to consider the unique characteristics of weak grids. Finally, a new technique is proposed for estimating system load relief factors that are used in aggregate single frequency stability models

Efficient design assessment in the railway electric infrastructure domain using cloud computing

Nowadays, railway infrastructure designers rely heavily on computer simulators and expert systems to model, analyze and evaluate potential deployments prior to their installation. This paper presents the railway power consumption simulator model (RPCS), a cloud-based model for the design, simulation and evaluation of railway electric infrastructures. This model integrates the parameters of an infrastructure within a search engine that generates and evaluates a set of simulations to achieve optimal designs, according to a given set of objectives and restrictions. The knowledge of the domain is represented as an ontology that translates the elements in the infrastructure into an electric circuit, which is simulated to obtain a wide range of electric metrics. In order to support the execution of thousands of scenarios in a scalable, efficient and fault-tolerant manner, this paper introduces an architecture to deploy the model in a cloud environment, and a dimensioning model to find the types and number of instances that maximize performance while minimizing the externalization costs. The resulting model is applied to a particular case study, allowing the execution of over one thousand concurrent experiments in a virtual cluster on the Amazon Elastic Compute Cloud.This work has been partially funded under the grant TIN2013-41350-P of the Spanish Ministry of Economics and Competitiveness, and the COST Action IC1305 ”Network for Sustainable Ultrascale Computing Platforms” (NESUS)