Abstract-Biogeography-based optimization (BBO) is a novel evolutionary algorithm that is based on the mathematics of biogeography. Biogeography is the study of the geographical distribution of biological organisms. In the BBO model, problem solutions are represented as islands, and the sharing of features between solutions is represented as immigration and emigration between the islands. This paper presents an application of the BBO algorithm to the power flow problem for an IEEE 30-bus Test Case system. The BBO solution is compared with the solution of the same problem using a genetic algorithm (GA). The results of Monte Carlo simulations indicate that the BBO algorithm consistently performs better than the GA in determining an optimal solution to the power flow problem.

SummaryThe widespread adoption of demand response (DR) enabled appliances and thermostats can result in a significant reduction to peak electrical demand and provide potential grid stabilization benefits. GE Appliances has developed a line of appliances that will have the capability of offering several levels of demand reduction actions based on information received from the utility grid, often in the form of price or grid status. However due to a number of factors, including the number of DR-enabled appliances available at any given time, the reduction of diversity factor due to the synchronizing control signal, and the percentage of consumers who may override the utility signal, it can be difficult to predict the aggregate response of a large number of residences. The effects of these behaviors can be modeled and simulated in the Pacific Northwest National Laboratory (PNNL) developed open-source software, GridLAB-D™, including evaluation of the appliance controls, improvement to current algorithms, and development of aggregate control methodologies.This report is the first in a series of three reports describing the potential of GE Appliances' DR-enabled appliances to provide benefits to the utility grid. The first report will describe the modeling methodology used to represent the appliances in the GridLAB-D simulation environment and the estimated potential for peak demand reduction at various deployment levels. The second and third reports will explore the potential of aggregated group actions to positively impact grid stability, including frequency and voltage regulation and spinning reserves, and the impacts on distribution feeder voltage regulation, including mitigation of fluctuations caused by high penetration of photovoltaic distributed generation and the effects on volt-var control schemes.In Section 2, the effects and potential benefits of appliances on the power system were studied by modeling GE Appliances' DR-enabled appliances in GridLAB-D. GridLAB-D is an open-source, state-of-the-art software designed at PNNL for the Department of Energy's Office of Electricity Delivery and Energy Reliability to simulate the complexities of the smart grid from the substation down to the end-use load. Multi-state appliance models were used to represent not only the baseline instantaneous power demand and energy consumption, but the control systems developed by GE Appliances. This enabled the modeled appliances to respond to load reduction signals, as well as the change in behavior of the appliance in response to the signal. This included the power and energy consumption and the time horizon over which they operate for the various operational modes, and how changes in the DR control signal affect load behavior. This gives insight into the potential for short term versus longer term reduction in power consumption, and allows for exploration of different DR control signals without developing a new model for each case. Additionally, it gives insight into how to improve the effectiveness of the DR-enabled appliances...

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Detection and timely identification of power system disturbances are essential for situation awareness and reliable electricity grid operation. Because records of actual events in the system are limited, ensemble simulation-based events are needed to provide adequate data for building event-detection models through deep learning; e.g., a convolutional neural network (CNN). An ensemble numerical simulation-based training data set have been generated through dynamic simulations performed on the Polish system with various types of faults in different locations. Such data augmentation is proven to be able to provide adequate data for deep learning. The synchronous generators’ frequency signals are used and encoded into images for developing and evaluating CNN models for classification of fault types and locations. With a time-domain stacked image set as the benchmark, two different time-series encoding approaches, i.e., wavelet decomposition-based frequency-domain stacking and polar coordinate system-based Gramian Angular Field (GAF) stacking, are also adopted to evaluate and compare the CNN model performance and applicability. The various encoding approaches are suitable for different fault types and spatial zonation. With optimized settings of the developed CNN models, the classification and localization accuracies can go beyond 84 and 91%, respectively.