Related papers: Machine-Learning Inspired Clustering of Distribute…
The present study proposes clustering techniques for designing demand response (DR) programs for commercial and residential prosumers. The goal is to alter the consumption behavior of the prosumers within a distributed energy community in…
As electrical generation becomes more distributed and volatile, and loads become more uncertain, controllability of distributed energy resources (DERs), regardless of their ownership status, will be necessary for grid reliability. Grid…
We consider the optimization of distributed resource scheduling to minimize the sum of task latency and energy consumption for all the Internet of things devices (IoTDs) in a large-scale mobile edge computing (MEC) system. To address this…
As photovoltaic (PV) penetration continues to rise and smart inverter functionality continues to expand, smart inverters and other distributed energy resources (DERs) will play increasingly important roles in distribution system power…
In regions with high shares of distributed energy resources (DERs), massive disconnection of small-scale DERs in low-voltage distribution grids during disturbances poses a serious threat to power system security. However, modeling this…
Clustering under pairwise constraints is an important knowledge discovery tool that enables the learning of appropriate kernels or distance metrics to improve clustering performance. These pairwise constraints, which come in the form of…
The massive integration of renewable-based distributed energy resources (DERs) inherently increases the energy system's complexity, especially when it comes to defining its operational schedule. Deep reinforcement learning (DRL) algorithms…
This paper addresses active state estimation with a team of robotic sensors. The states to be estimated are represented by spatially distributed, uncorrelated, stationary vectors. Given a prior belief on the geographic locations of the…
Over the past years, distributed energy resources (DER) have been the object of many studies, which recognise and establish their emerging role in the future of power systems. However, the implementation of many scenarios and mechanism are…
The integration of distributed energy resources (DER) makes active distribution networks (ADNs) natural providers of flexibility services. However, the optimal operation of flexible units in ADNs is highly complex, which poses challenges…
Many cluster management systems (CMSs) have been proposed to share a single cluster with multiple distributed computing systems. However, none of the existing approaches can handle distributed machine learning (ML) workloads given the…
With the rapid adoption of emerging inverter-based resources, it is crucial to understand their dynamic interactions across the network and ensure stability. This paper proposes a systematic and efficient method to determine the optimal…
The large-scale integration of renewable generation directly affects the reliability of power grids. We investigate the problem of power balancing in a general renewable-integrated power grid with storage and flexible loads. We consider a…
Deep learning and especially the use of Deep Neural Networks (DNNs) provides impressive results in various regression and classification tasks. However, to achieve these results, there is a high demand for computing and storing resources.…
Software-defined networks (SDNs) are a huge evolution in simplifying implementation and network operation which have reduced costs and made the network programmable. Although SDNs are a suitable option for solving some of the previous…
With the rising adoption of distributed energy resources (DERs), microgrid dispatch is facing new challenges: DER owners are independent stakeholders seeking to maximize their individual profits rather than being controlled centrally; and…
We propose an algorithm for distributed charging control of electric vehicles (EVs) using online learning and online convex optimization. Many distributed charging control algorithms in the literature implicitly assume fast two-way…
Motivated by the penetration of converter-based generation into the electrical grid, we revisit the classical log-linear learning algorithm for optimal allocation {of synchronous machines and converters} for mixed power generation. The…
Distributed generation is widely being utilized, so the basic theme of this research is to have a hands-on experience to synchronize a Distributed Energy Resource (DER) to the Mains Grid. A control algorithm is implemented for energy supply…
Most existing personalized federated learning approaches are based on intricate designs, which often require complex implementation and tuning. In order to address this limitation, we propose a simple yet effective personalized federated…