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This article presents a mixed-integer second-order cone programming model to determine the optimal sizing of a community-shared photovoltaic and battery energy storage system (PV-BESS) within a peer-to-peer (P2P) energy trading framework.…
This letter proposes an enhanced sufficient battery model (ESBM) as well as a binary search algorithm for a sharp inner-approximation of the aggregate flexibility of thermostatically controlled load (TCL) arrays. Compared with the previous…
This paper proposes an input convex neural network (ICNN)-Assisted optimal power flow (OPF) in distribution networks. Instead of relying purely on optimization or machine learning, the ICNN-Assisted OPF is a combination of optimization and…
Recent years have seen rapid increases in intermittent renewable generation, requiring novel battery energy storage systems (BESS) solutions. One recent trend is the emergence of large grid-connected batteries, that can be controlled to…
Fast charging has attracted increasing attention from the battery community for electrical vehicles (EVs) to alleviate range anxiety and reduce charging time for EVs. However, inappropriate charging strategies would cause severe degradation…
The modeling of multi-energy systems (MES) is the basic task of analyzing energy systems integration. The variable energy efficiencies of the energy conversion and storage components in MES introduce nonlinearity to the model and thus…
Electricity load forecasting plays an important role in the energy planning such as generation and distribution. However, the nonlinearity and dynamic uncertainties in the smart grid environment are the main obstacles in forecasting…
This paper proposes and experimentally validates a two-stage scheduling and control strategy for a behind-the-meter battery energy storage system (BESS) delivering both local and grid services. Considered services are the maximization of PV…
This paper presents the input convex neural network architecture. These are scalar-valued (potentially deep) neural networks with constraints on the network parameters such that the output of the network is a convex function of (some of)…
We develop the theory of Energy Conserving Descent (ECD) and introduce ECDSep, a gradient-based optimization algorithm able to tackle convex and non-convex optimization problems. The method is based on the novel ECD framework of…
The concept of integrating physics-based and data-driven approaches has become popular for modeling sustainable energy systems. However, the existing literature mainly focuses on the data-driven surrogates generated to replace physics-based…
In this paper, we propose a low-power hardware for efficient deployment of binarized neural networks (BNNs) that have been trained for physiological datasets. BNNs constrain weights and feature-map to 1 bit, can pack in as many 1-bit…
Due to the increasing occurrence of natural disasters, importance of maintaining sustainable energy for cities and society is felt more than ever. On the other hand, power loss reduction is a challenging issue of active distribution…
This paper presents an innovative offshore solution where oil & gas platform clusters are powered by a wind farm and a hydrogen hub. The results show a feasible off-grid design as an alternative to conventional electrification solutions. To…
In this work, we employ neural architecture search (NAS) to enhance the efficiency of deploying diverse machine learning (ML) tasks on in-memory computing (IMC) architectures. Initially, we design three fundamental components inspired by…
A major concern associated to the massive connection of distributed energy resources is the increasing share of power electronic interfaces resulting in the global inertia reduction of power systems. The recent literature advocated the use…
Battery swapping as a business model for battery energy storage (BES) has great potential in future integrated low-carbon energy and transportation systems. However, frequent battery swapping will inevitably accelerate battery degradation…
In power system dynamic simulation, up to 90% of the computational time is devoted to solve the network equations, i.e., a set of linear equations. Traditional approaches are based on sparse LU factorization, which is inherently sequential.…
Capsule Networks (CapsNets) are brand-new architectures that have shown ground-breaking results in certain areas of Computer Vision (CV). In 2017, Hinton and his team introduced CapsNets with routing-by-agreement in "Sabour et al" and in a…
This paper presents a stochastic optimization model for planning the participation of battery energy storage systems (BESSs) in energy and regulation markets. The proposed model quantifies and compares the business value of single and…