Optimal siting and sizing of distributed energy storage systems via alternating direction method of multipliers

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Renewable energy communities, where citizens, businesses, and institutions produce, consume, store, and share energy, are increasingly pivotal in energy markets. The use of shared community batteries introduces the challenge of adapting control strategies ...

The thesis explores the issue of fairness in the real-time (RT) control of battery energy storage systems (BESSs) hosted in active distribution networks (ADNs) in the presence of uncertainties by proposing and experimentally validating appropriate control ...

This work presents a CMOS-compatible multilayer platform integrating energy storage and tunable varactor functionality within a compact system. By co-integrating antiferroelectric (AFE) and dielectric (DE) layers, the design achieves tunability up to 28%, ...

High-power tunable lasers are intensely pursued due to their vast application potential such as in telecom, ranging, and molecular sensing. Integrated photonics, however, is usually considered not suitable for high-power applications mainly due to its smal ...

In this paper, we propose an analytical stochastic dynamic programming (SDP) algorithm to address the optimal management problem of price-maker community energy storage. As a price-maker, energy storage smooths price differences, thus decreasing energy arb ...

Reversible Solid Oxide electrolyser (rSOE) is a promising solution for long-duration energy storage due to its exceptional features such as inherent reversibility, co-electrolysis capability in electrolysis mode, high efficiency, adaptability to multiple f ...

Accurate forecasting of photovoltaic (PV) power production is crucial for the integration of more renewable energy sources into the power grid. PV power production is highly intermittent, due to the stochastic cloud behaviour and cloud dynamics. Previous w ...

Ultraviolet-induced degradation (UV-ID) of various PV cell types was analyzed under optical UV filters with different cutoff wavelengths. Cell types studied included interdigitated back contact (IBC), passivated emitter and rear totally diffused (PERT), an ...

The ability to train ever-larger neural networks brings artificial intelligence to the forefront of scientific and technical discoveries. However, their exponentially increasing size creates a proportionally greater demand for energy and computational hard ...

The electrification of the transport sector is a key element in decarbonizing our societies. However, energy systems will have to cope with additional electricity demand due to the charging needs of electric vehicles (EVs) together with the integration of ...

This paper proposes a novel integer-mixed probabilistic optimal power flow (IM-POPF) strategy for frequency regulation in islanded microgrids under uncertain operating conditions. Existing load-shedding approaches face critical limitations: continuous fram ...

Deploying energy-efficient deep neural networks on energy-constrained edge devices is an important research topic in both machine learning and circuit design communities. Both artificial neural networks (ANNs) and spiking neural networks (SNNs) have been p ...

The shift towards DC power distribution networks, enabled by power electronics technologies, is changing the nature of electrical power systems. Nowadays, DC power distribution networks can effectively support the high penetration of distributed energy res ...

Sorbent are the workhorses of most contemporary separation processes. Crystalline sorbents boast descript structures that facilitate in silico study. Their diversity is matched by their varied applications. At the forefront of their currently researched ap ...

The global imperative to transition from fossil fuel-based energy sources to renewable alternatives has become increasingly urgent in the pursuit of sustainable and renewable power generation. This paradigm shift necessitates innovative approaches to manag ...

Fuel cells are considered one of the most efficient and environmental-friendly technologies available for converting chemical energy into electricity and heat, and considered a key technology to close the hydrogen cycle. In particular, combined heat and po ...

In recent years, many efforts have been made to find alternative renewable energy sources that can ideally replace the use of fossil fuels in all aspects. One of the new emerging energy technologies is the bioelectrochemical system, of which two types are ...

This paper investigates the fundamental regression task of learning k neurons (a.k.a. teachers) from Gaussian input, using two-layer ReLU neural networks with width m (a.k.a. students) and m, k = O(1), trained via gradient descent under proper initializati ...

Optimal siting and sizing of distributed energy storage systems via alternating direction method of multipliers

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Fair real-time control of energy storage systems in active distribution networks in the presence of uncertainties

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Energy Management of Price-Maker Community Energy Storage by Stochastic Dynamic Programming

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Probabilistic Load-Shedding Strategy for Frequency Regulation in Microgrids Under Uncertainties

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DC Power Distribution Networks with Direct Current Transformers

Harnessing modular approaches in the study of crystalline sorbents

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