Related papers: EnergyTwin: A Multi-Agent System for Simulating an…
Smart energy networks provide for an effective means to accommodate high penetrations of variable renewable energy sources like solar and wind, which are key for deep decarbonisation of energy production. However, given the variability of…
Due to the threat of climate change, a transition from a fossil-fuel based system to one based on zero-carbon is required. However, this is not as simple as instantaneously closing down all fossil fuel energy generation and replacing them…
Energy harvesting technologies offer a promising solution to sustainably power an ever-growing number of Internet of Things (IoT) devices. However, due to the weak and transient natures of energy harvesting, IoT devices have to work…
Understandable and persuasive recommendations support the electricity consumers' behavioral change to tackle the energy efficiency problem. Generating load shifting recommendations for household appliances as explainable increases the…
Smart buildings have great potential for shaping an energy-efficient, sustainable, and more economic future for our planet as buildings account for approximately 40% of the global energy consumption. Future of the smart buildings lies in…
Power systems are subject to fundamental changes due to the increasing infeed of decentralised renewable energy sources and storage. The decentralised nature of the new actors in the system requires new concepts for structuring the power…
Mobile power sources (MPSs) have been gradually deployed in microgrids as critical resources to coordinate with repair crews (RCs) towards resilience enhancement owing to their flexibility and mobility in handling the complex coupled…
"Geographic Load Balancing" is a strategy for reducing the energy cost of data centers spreading across different terrestrial locations. In this paper, we focus on load balancing among micro-datacenters powered by renewable energy sources.…
The increasing adoption of renewable energy sources increases the need for balancing power. The security concerns of the distribution system operators are increasing due to fast adoption of distributed energy resources. So far, various…
Decentralised online learning enables runtime adaptation in cyber-physical multi-agent systems, but when operating conditions change, learned policies often require substantial trial-and-error interaction before recovering performance. To…
Contemporary electricity distribution systems are being challenged by the variability of renewable energy sources. Slow response times and long energy management periods cannot efficiently integrate intermittent renewable generation and…
This paper proposes an agent-based model that combines both spot and balancing electricity markets. From this model, we develop a multi-agent simulation to study the integration of the consumers' flexibility into the system. Our study…
A Digital Twin (DT) is a simulation of a physical system that provides information to make decisions that add economic, social or commercial value. The behaviour of a physical system changes over time, a DT must therefore be continually…
This paper presents a Reinforcement Learning (RL) based energy market for a prosumer dominated microgrid. The proposed market model facilitates a real-time and demanddependent dynamic pricing environment, which reduces grid costs and…
End-to-end training of multi-agent systems offers significant advantages in improving multi-task performance. However, training such models remains challenging and requires extensive manual design and monitoring. In this work, we introduce…
To enable integrating social equity considerations in infrastructure resilience assessments, this study created a new computational multi-agent simulation model which enables integrated assessment of hazard, infrastructure system, and…
Global warming is endangering the earth's ecosystem. It is imperative for us to limit green house gas emissions in order to combat rising global average temperatures. One way to move forward is the integration of renewable energy resources…
The design of new control strategies for future energy systems can neither be directly tested in real power grids nor be evaluated based on only current grid situations. In this regard, extensive tests are required in laboratory settings…
This paper proposes a novel scalable type of multi-agent reinforcement learning-based coordination for distributed residential energy. Cooperating agents learn to control the flexibility offered by electric vehicles, space heating and…
An increasing number of smart devices controlling loads opens a potential pathway for false data attacks which could alter the loads. The presence of energy storage with its ability to quickly respond to discrepancies in loads offers a…