[HTML][HTML] Seasonal thermal energy storage in smart energy systems: District-level applications and modelling approaches
Seasonal thermal energy storage can provide flexibility to smart energy systems and are
characterised by low cost per unit energy capacity and varying applicability to different …
characterised by low cost per unit energy capacity and varying applicability to different …
From green hydrogen to electricity: A review on recent advances, challenges, and opportunities on power-to-hydrogen-to-power systems
A Risco-Bravo, C Varela, J Bartels… - … and Sustainable Energy …, 2024 - Elsevier
The energy sector is responsible for around two-thirds of greenhouse gas emissions, mainly
relying on fossil fuels. Thus, the industry must make substantial changes as part of the global …
relying on fossil fuels. Thus, the industry must make substantial changes as part of the global …
Multi-agent reinforcement learning for active voltage control on power distribution networks
This paper presents a problem in power networks that creates an exciting and yet
challenging real-world scenario for application of multi-agent reinforcement learning …
challenging real-world scenario for application of multi-agent reinforcement learning …
Simbench—a benchmark dataset of electric power systems to compare innovative solutions based on power flow analysis
S Meinecke, D Sarajlić, SR Drauz, A Klettke… - Energies, 2020 - mdpi.com
Publicly accessible, elaborated grid datasets, ie, benchmark grids, are well suited to publish
and compare methods or study results. Similarly, developing innovative tools and algorithms …
and compare methods or study results. Similarly, developing innovative tools and algorithms …
PyPSA: Python for power system analysis
Python for Power System Analysis (PyPSA) is a free software toolbox for simulating and
optimising modern electrical power systems over multiple periods. PyPSA includes models …
optimising modern electrical power systems over multiple periods. PyPSA includes models …
Optimal energy management strategies for energy Internet via deep reinforcement learning approach
This paper investigates the energy management problem in the field of energy Internet (EI)
with interdisciplinary techniques. The concept of EI has been proposed for a while. However …
with interdisciplinary techniques. The concept of EI has been proposed for a while. However …
Data-driven dynamical control for bottom-up energy Internet system
With the increasing concern on climate change and global warming, the reduction of carbon
emission becomes an important topic in many aspects of human society. The development …
emission becomes an important topic in many aspects of human society. The development …
[HTML][HTML] Opening the black box of energy modelling: Strategies and lessons learned
The global energy system is undergoing a major transition, and in energy planning and
decision-making across governments, industry and academia, models play a crucial role …
decision-making across governments, industry and academia, models play a crucial role …
The opportunity for smart charging to mitigate the impact of electric vehicles on transmission and distribution systems
A rapid increase in the number of electric vehicles is expected in coming years, driven by
government incentives and falling battery prices. Charging these vehicles will add significant …
government incentives and falling battery prices. Charging these vehicles will add significant …
A modified particle swarm optimization using adaptive strategy
In expert systems, complex optimization problems are usually nonlinear, nonconvex,
multimodal and discontinuous. As an efficient and simple optimization algorithm, particle …
multimodal and discontinuous. As an efficient and simple optimization algorithm, particle …