Software Agents for Collaborating Smart Solar-Powered Micro-Grids

Amato, Alba; Aversa, Rocco; Martino, Beniamino Di; Scialdone, Marco; Venticinque, Salvatore; Hallsteinsen, Svein; Horn, Geir

doi:10.1007/978-3-319-07040-7_14

Cited by 18 publications

(5 citation statements)

References 5 publications

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“…This development has been shown to reduce battery capacity and energy losses [15]. The related effort focused on the computational properties of the negotiation algorithms [16], the optimised scheduling of energy storage system (ESS) using automated negotiations [17], multi-commodity optimisation of energy traders' resources [18] and prosumers' behavioural patterns based on their bidding strategies. Other market clearing algorithms include the use of distributed optimisation methods [19], consensus-based optimisation [20], dual-based prices like alternating direction method of multipliers (ADMM) [21], reinforcement learning [22] and double-auction for matching prosumers in the market.…”

Section: Review Of P2p Energy Trading Modelsmentioning

confidence: 99%

VirtElect: A Peer-to-Peer Trading Platform for Local Energy Transactions

Jogunola

Tsado

Adebisi

et al. 2022

IEEE Internet Things J.

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An average UK electricity bill is made up of at least 60% service charge, with approximately 22% related to network characteristics including distance charge. This makes distance and network constraints important factors in matching prosumers on any peer-to-peer energy trading platform as assessed in this paper. To realise that, A platform -V irtElect, based on a double auction market is developed to support the matching interaction between prosumers. Case studies based on real microgrid data are used to verify the performance of the platform in demonstrating the potential of local energy consumption. Results show that it is possible to balance local energy generation and consumption, with little or no interaction with the utility grid. We also show that local energy trading is not only beneficial to the environment but also leads to a significant amount of cost savings of up to 45%, depending on the number of participants and their ratios on the platform.

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Section: Review Of P2p Energy Trading Modelsmentioning

confidence: 99%

VirtElect: A Peer-to-Peer Trading Platform for Local Energy Transactions

Jogunola

Tsado

Adebisi

et al. 2022

IEEE Internet Things J.

View full text Add to dashboard Cite

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“…6. The CoSSMic (Collaborating Smart Solar-powered Microgrids) dataset 3 [37] contains sub-metered energy demand for 11 households in Konstanz, Germany. The energy demand is sampled at 1 minute intervals, and is available between October 2013 to December 2016.…”

Section: Residential Building Energy Demandmentioning

confidence: 99%

Towards data-driven energy communities: A review of open-source datasets, models and tools

Kazmi

Munné‐Collado

Mehmood

et al. 2021

Renewable and Sustainable Energy Reviews

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Energy communities will play a central role in the sustainable energy transition by helping inform and engage end users to become more responsible consumers of energy. However, the true potential of energy communities can only be unlocked at scale. This scalability requires data-driven solutions that model not just the behaviour of building occupants but also of energy flexible resources in buildings, distributed generation and grid conditions in general. This understanding can then be utilized to improve the design and operation of energy communities in a variety of real-world settings. However, in practice, collecting and analysing the data necessary to realize these objectives forms a large part of such projects, and is often seen as a prohibitive stumbling block. Furthermore, without a proper understanding of the local context, these projects are often at risk of failure due to misplaced expectations. However, this process can be considerably accelerated by utilizing open source datasets and models from related projects, which have been carried out in the past. Likewise, a number of open source, general-purpose tools exist that can help practitioners design and operate LECs in a near-optimal manner. These resources are important because they not only help ground expectations, they also provide LECs and other relevant stakeholders, including utilities and distribution system operators, with much-needed visibility on future energy and cash flows. This review provides a detailed overview of these open-source datasets, models and tools, and the many ways they can be utilized in optimally designing and operating real-world energy communities. It also highlights some of the most important limitations in currently available open source resources, and points to future research directions. Highlights:1. The importance of open-source datasets and tools for local energy communities 2. Common use cases for open-source datasets, models and tools for energy communities 3. A thorough review of electricity demand and meteorological datasets and models 4. Most important shortcomings with currently available datasets, models and tools

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“…To demonstrate the impact of charge flexibility on loads, we use research data from the CoSSMic research project [10] and present three examples with charging at a charge station with a maximum capacity of 6 kW. The distribution of power among the CPs is controlled, and the charging of EVs is started and stopped accordingly.…”

Section: Example On Use Of Indicatorsmentioning

confidence: 99%

Evaluation Approach for Smart Charging Ecosystem – with Focus on Automated Data Collection and Indicator Calculations

Natvig

Jiang

Hallsteinsen

et al. 2021

Advanced Information Networking and Applications

Self Cite

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Access to charging is a prerequisite for the transition to electric mobility. There are however challenges related to charging and charging infrastructures, e.g., charging availability, grid capacity during peak hours, and the CO2 intensity of the energy mix provided. This paper suggests measures to be taken in a smart charging ecosystem to mitigate the challenges. The impact of the measures must however be evaluated. The objective of the paper is to suggest an evaluation approach, with focus on quantitative aspects. The measures of relevance, the associated indicators for the impact evaluation, and an overview of the research data needed is provided. In addition, data content examples and calculation details are described for two indicators -the charging flexibility provided by the EV users and the peak to average ratio characterising the load balancing. Scenarios to be evaluated and how simulations are used to complement the evaluation of the demonstrators are addressed.

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Software Agents for Collaborating Smart Solar-Powered Micro-Grids

Cited by 18 publications

References 5 publications

VirtElect: A Peer-to-Peer Trading Platform for Local Energy Transactions

VirtElect: A Peer-to-Peer Trading Platform for Local Energy Transactions

Towards data-driven energy communities: A review of open-source datasets, models and tools

Evaluation Approach for Smart Charging Ecosystem – with Focus on Automated Data Collection and Indicator Calculations

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