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Energy storage plays a more important role than ever before, due to the transition to smart grid along with higher penetration of renewable resources. In this paper, we describe our optimal nonlinear battery control algorithm that can handle multiple batteries connected to the grid in a distributed and cost-optimal fashion, while maintaining low complexity of $O(N^{2})$ . In contrast to the state-of-the-art...
Modern power grid has evolved from a passive network into an application of Internet of Things with numerous interconnected elements and users. In this environment, household users greatly benefit from a prediction algorithm that estimates their future power demand to help them control off-grid generation, battery storage, and power consumption. In particular, household power consumption prediction...
The Internet of Things (IoT) has brought increased sensing, monitoring and actuation capabilities to several domains including residential buildings. Residential energy management methods can leverage these capabilities and devise smarter solutions. This requires processing and reasoning data constantly generated by various IoT devices. In this paper, we use a hierarchical system model for IoT-based...
The increasingly pervasive deployment of networked sensors in the Smart Grid for monitoring energy consumption has resulted in an unprecedentedly large amount of data generation. Efficient methods are required to understand this high volume and high dimensional data on an embedded platform, which has many challenges due to memory, processing and power constraints. One of the popular methods to analyze...
The Internet of Things is emerging as more wireless sensor networks get deployed every day, pushing decision making and processing towards the edge onto more constrained devices. In these constrained networks, using a single packet to transmit small sensed data is an inefficient use of both bandwidth and energy. Aggregating multiple measurements and packets into a single packet increases efficiency...
The Internet of Things (IoT) refers to an environment of ubiquitous sensing and actuation, where devices are connected to a distributed backend infrastructure. It offers the opportunity to access a large amount of input data, and process it into contextual information about different system entities for reasoning and actuation. State-of-the-art IoT applications are generally black-box, end-to-end...
The Smart Grid is drawing attention from various research areas. Distributed control algorithms at different scales within the grid are being developed and deployed; yet their effects on each other and the grid's health and stability has not been sufficiently studied due to the lack of a capable simulator. Simulators in the literature can solve the power flow by modeling the physical system, but fail...
With the integration of renewable energy sources and large-scale smart buildings, the electricity grid becomes more prone to instabilities due to unexpected fluctuations in energy consumption. Data centers are a type of smart building because of their innate automation and controllable load. Load controlling in data centers has been studied extensively with scheduling/migration, peak power shaving,...
With the increasing penetration of renewable energy resources within the Smart Grid, solar forecasting has become an important problem for hour-ahead and day-ahead planning. Within this work, we analyze the Analog Forecast method family, which uses past observations to improve the forecast product. We first show that the frequently used euclidean distance metric has drawbacks and leads to poor performance...
The need for a smarter grid is emerging with the increase of peak demand and the integration of renewable resources. A great solution for peak shifting and renewable energy smoothing is through the usage of energy storage devices. This paper focuses on the energy storage power control problem in small to medium sized power distribution systems with loads, energy storage devices and renewable resources...
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