Time-Series Analysis of Photovoltaic Distributed Generation Impacts on a Local Distributed Network

Increasing penetration level of photovoltaic (PV) distributed generation (DG) into distribution networks will have many impacts on nominal circuit operating conditions including voltage quality and reverse power flow issues. In U.S. most studies on PVDG impacts on distribution networks are performed for west coast and central states. The objective of this paper is to study the impacts of PVDG integration on local distribution network based on real-world settings for network parameters and time-series analysis. PVDG penetration level is considered to find the hosting capacity of the network without having major issues in terms of voltage quality and reverse power flow. Time-series analyses show that distributed installation of PVDGs on commercial buses has the maximum network energy loss reduction and larger penetration ratios for them. Additionally, the penetration ratio thresholds for which there will be no power quality and reverse power flow issues and optimal allocation of PVDG and penetration levels are identified for different installation scenarios.Comment: To be published (Accepted) in: 12th IEEE PES PowerTech Conference, Manchester, UK, 201

Policy Conflict Analysis in Distributed System Management

Accepted versio

Distributed Formal Concept Analysis Algorithms Based on an Iterative MapReduce Framework

While many existing formal concept analysis algorithms are efficient, they are typically unsuitable for distributed implementation. Taking the MapReduce (MR) framework as our inspiration we introduce a distributed approach for performing formal concept mining. Our method has its novelty in that we use a light-weight MapReduce runtime called Twister which is better suited to iterative algorithms than recent distributed approaches. First, we describe the theoretical foundations underpinning our distributed formal concept analysis approach. Second, we provide a representative exemplar of how a classic centralized algorithm can be implemented in a distributed fashion using our methodology: we modify Ganter's classic algorithm by introducing a family of MR* algorithms, namely MRGanter and MRGanter+ where the prefix denotes the algorithm's lineage. To evaluate the factors that impact distributed algorithm performance, we compare our MR* algorithms with the state-of-the-art. Experiments conducted on real datasets demonstrate that MRGanter+ is efficient, scalable and an appealing algorithm for distributed problems.Comment: 17 pages, ICFCA 201, Formal Concept Analysis 201