Energy management: flexibility, risk and optimization

Moriarty, John; Mancarella, Pierluigi

doi:10.1098/rsta.2017.0052

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…Intelligent solutions for control and operation of the various individual components that comprise an urban energy system have become increasingly prevalent [10]. Often driven by the goals of reducing energy consumption, emissions or cost [11], while maintaining robustness to uncertainty [12], such approaches seek to leverage the key enablers of increased computational power and data ubiquity, as well as artificial intelligence (AI), machine learning and advanced control techniques [12]. These approaches need not be solely characterised as 'Smart City' related interventions and are often considered in an isolated manner.…”

Section: Review Of Existing Concepts and Applications In Smart Energymentioning

confidence: 99%

Smart energy systems for sustainable smart cities: Current developments, trends and future directions

et al. 2019

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Within the context of the Smart City, the need for intelligent approaches to manage and coordinate the diverse range of supply and conversion technologies and demand applications has been well established. The wide-scale proliferation of sensors coupled with the implementation of embedded computational intelligence algorithms can help to tackle many of the technical challenges associated with this energy systems integration problem. Nonetheless, barriers still exist, as suitable methods are needed to handle complex networks of actors, often with competing objectives, while determining design and operational decisions for systems across a wide spectrum of features and time-scales. This review looks at the current developments in the smart energy sector, focussing on techniques in the main application areas along with relevant implemented examples, while highlighting some of the key challenges currently faced and outlining future pathways for the sector. A detailed overview of a framework developed for the EU H2020 funded Sharing Cities project is also provided to illustrate the nature of the design stages encountered and control hierarchies required. The study aims to summarise the current state of computational intelligence in the field of smart energy management, providing insight into the ways in which current barriers can be overcome.

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Section: Review Of Existing Concepts and Applications In Smart Energymentioning

confidence: 99%

Smart energy systems for sustainable smart cities: Current developments, trends and future directions

et al. 2019

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“…Due to the intermittency and stochasticity of renewable energy generation and demand, maintaining grid balance is regarded to as one of the major challenges. In this context, Smart Grid technologies like Energy Storage Systems (ESS), smart control infrastructure and algorithms for energy management systems are expected to play an essential role in the power systems, [2]. Moreover, according to the European Technology Platform of Smart Grids, this topic is seen as one of the main Priority Project Concepts of ETIP SNET's R&D implementation plan 2022-2025 [3].…”

Section: Introductionmentioning

confidence: 99%

Development and Initial Testing of a Virtual Laboratory for the Build up and Testing of Microgrid Management Algorithms

Fernández¹,

Menéndez²,

Meneses³

et al. 2024

RE&PQJ

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In a bid of facilitating the increasing penetration of intermittent and random renewable energies, microgrids along with their management algorithms are becoming crucial assets. To prove their effectiveness, these algorithms need to be tested in real environments and/or laboratories, which can be very difficult in many cases, especially at the initial development stages. To solve this issue, this article proposes the use of a laboratory digital twin, i.e., a virtual laboratory with a behaviour that is similar to that of real installations, aimed at facilitating the development, testing and debugging of microgrids management algorithms. The proposed solution is demonstrated to be safe and complete when it comes to test these algorithms.

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Optimal valuation of wind energy projects co-located with battery storage

et al. 2021

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Energy management: flexibility, risk and optimization

Abstract: One contribution of 13 to a theme issue 'Energy management: flexibility, risk and optimization'.

Cited by 4 publications

References 16 publications

Smart energy systems for sustainable smart cities: Current developments, trends and future directions

Smart energy systems for sustainable smart cities: Current developments, trends and future directions

Development and Initial Testing of a Virtual Laboratory for the Build up and Testing of Microgrid Management Algorithms

Optimal valuation of wind energy projects co-located with battery storage

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