There are advantages of installing distributed generation (DG) in distribution systems: for example, improving reliability, mitigating voltage sags, unloading subtransmission and transmission system, and sometimes utilizing renewables. All of these factors have resulted in an increase in the use of DGs. However, the increase of fault currents in power systems is a consequence of the appearance of new generation sources. Some operating and planning limitations may be imposed by the resulting fault currents. This paper discusses a model of inverter based DGs which can be used to analyze the dynamic performance of power systems in the presence of DGs. In a style similar to protective relaying analysis, three-dimensional plots are used to depict the behavior of system reactance (X) and resistance (R) versus time. These plots depict operating parameters in relation to zones of protection, and this information is useful for the coordination of protection systems in the presence of DG

Building energy management systems (BEMS) must consider energy utilization efficiency improvement, energy cost reduction, and renewable energy technology utilization in order to serve local energy loads in building structures with dispersed resources. The distributed management of building energy system proposed in this paper utilizes a semi-centralized decision-making methodology using multi-agent systems for BEMS for electrical, heating, and cooling energy zones with combined heat and power system optimizations aimed at improving energy efficiency and reducing energy costs. A case study is presented to demonstrate the validity of the proposed energy management scheme.

The Hilbert-Huang method is presented with modifications, for time-frequency analysis of distorted power quality signals. The empirical mode decomposition (EMD) is enhanced with masking signals based on fast Fourier transform (FFT), for separating frequencies that lie within an octave. Further, the instantaneous frequency and amplitude of the constituent modes obtained by Hilbert spectral analysis are improved by demodulation. The method shows promising time-frequency-magnitude localization capabilities for distorted power quality signals. The performance of the new technique is compared with that of another multiresolution analysis tool, the S-transform-a phase corrected wavelet transform. Analysis on actual measurements of transformer inrush current from an existing laboratory setup is used to demonstrate this technique.

Disasters, whether natural or man-made, compromise the quality of life for all involved. In such situations, expeditious recovery activities are deemed imperative and irreplaceable for the restoration of normalcy. However, recovery activities rely heavily on the critical infrastructures that supply basic needs like electricity, water, information, and transportation. When disasters strike, it is likely that the critical infrastructures themselves are affected significantly, hampering efficient recovery processes, thus presenting a Catch-22 conundrum. In this article, we present examples from different parts of the world where distributed energy resources, organized in a microgrid, were used to provide reliable electricity supply in the wake of disasters, allowing recovery and rebuilding efforts to occur with relatively greater efficiency.

We utilize a for-profit aggregator-based residential demand response (DR) approach to the smart grid resource allocation problem. The aggregator entity, using a given set of schedulable residential customer assets (e.g., smart appliances), must set a schedule to optimize for a given objective. Here, we consider optimizing for the profit of the aggregator. To encourage customer participation in the residential DR program, a new pricing structure named customer incentive pricing (CIP) is proposed. The aggregator profit is optimized using a proposed heuristic framework, implemented in the form of a genetic algorithm, that must determine a schedule of customer assets and the CIP. To validate our heuristic framework, we simulate the optimization of a large-scale system consisting of 5555 residential customer households and 56 642 schedulable assets using real-pricing data over a period of 24-h. We show that by optimizing purely for economic reasons, the aggregator can enact a beneficial change on the load profile of the overall power system.