AI Modelling and Time-series Forecasting Systems for Trading Energy Flexibility in Distribution Grids

Eck, Bradley; Fusco, Francesco; Gormally, Robert; Purcell, Mark; Tirupathi, Seshu

doi:10.1145/3307772.3330158

Cited by 7 publications

(10 citation statements)

References 4 publications

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“…Castor is a cloud-native system for the management of time series data and models that makes extensive use of serverless technology, in particular IBM Cloud Functions ( [25] [26]). Castor was previously applied to renewable energy forecasting and distribution grid optimization ( [27], [28]). The present aquaculture application shares several characteristics that motivate the usage of Castor.…”

Section: B Castormentioning

confidence: 99%

Deployment and Management of Time Series Forecasts in Ocean Industry

O’Donncha

Akhriev

Eck

et al. 2021

2021 IEEE International Conference on Big Data (Big Data)

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Machine learning has not achieved the same degree of success in environmental applications as in other industries. Challenges around data sparsity, quality, and consistency have limited the impact of deep neural network approaches and restricted the focus to research applications. An alternative approach -that is more amenable to the characteristics of data coming from disparate IoT devices deployed at different times and locations in the ocean -is to develop many lightweight models that can be readily scaled up or down based on the number of devices available at any time. This paper presents a serverless framework that naturally marries a single IoT sensor device with a forecasting model. Aspects related to data ingestion, data processing, model training and deployment are described. The framework is applied to a fish farm site in Atlantic Canada.

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Section: B Castormentioning

confidence: 99%

Deployment and Management of Time Series Forecasts in Ocean Industry

O’Donncha

Akhriev

Eck

et al. 2021

2021 IEEE International Conference on Big Data (Big Data)

Self Cite

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“…A set of smart-grid technologies to enable an energy flexibility market was developed within the context of the research project GOFLEX, funded by the European Union and involving a consortium of energy utilities, technology providers and research institutions across Europe [17], [18]. The flexibility market enabled residential or industrial electrical prosumers (consumers and producers) to actively participate in the energy system by offering to sell the flexibility in their energy production and/or consumption processes.…”

Section: Data Services For Energy Flexibility Marketsmentioning

confidence: 99%

“…A smart-grid pilot deployment of an energy flexibility market, as part of the research project [17], was available at the Electricity Authority of Cyprus, the operator of the electrical distribution network in Cyprus. As also overviewed in Section II, the project sought to develop data analytic-services for predicting localised congestions on the grid, in the form of voltage violations due to excessive distributed renewable generation, and eventually prevent them by issuing bids for purchasing energy flexibility on the market [18]. Live data were collected from July 2018 through to the end of 2019.…”

Section: A Available Data and Model Architecturementioning

confidence: 99%

Knowledge- and Data-driven Services for Energy Systems using Graph Neural Networks

Fusco¹,

Eck²,

Gormally³

et al. 2021

Preprint

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The transition away from carbon-based energy sources poses several challenges for the operation of electricity distribution systems. Increasing shares of distributed energy resources (e.g. renewable energy generators, electric vehicles) and internet-connected sensing and control devices (e.g. smart heating and cooling) require new tools to support accurate, datadriven decision making. Modelling the effect of such growing complexity in the electrical grid is possible in principle using state-of-the-art power-power flow models. In practice, the detailed information needed for these physical simulations may be unknown or prohibitively expensive to obtain. Hence, datadriven approaches to power systems modelling, including feedforward neural networks and auto-encoders, have been studied to leverage the increasing availability of sensor data, but have seen limited practical adoption due to lack of transparency and inefficiencies on large-scale problems. Our work addresses this gap by proposing a data-and knowledge-driven probabilistic graphical model for energy systems based on the framework of graph neural networks (GNNs). The model can explicitly factor in domain knowledge, in the form of grid topology or physics constraints, thus resulting in sparser architectures and much smaller parameters dimensionality when compared with traditional machine-learning models with similar accuracy. Results obtained from a real-world smart-grid demonstration project show how the GNN was used to inform grid congestion predictions and market bidding services for a distribution system operator participating in an energy flexibility market.

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“…Moreover, deep learning as a recently popular technique is utilized in cross-border electricity trading and price prediction [86]. The study of [94] shows the progress in using AI algorithms to assist distribution system operators (DSO) in managing high levels of renewables on a local flexibility trading market.…”

Section: Using Ai To Enhance Value Provisioning On the Energy Platformmentioning

confidence: 99%

Electricity Market Empowered by Artificial Intelligence: A Platform Approach

Ahokangas

Louis

et al. 2019

Energies

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Artificial intelligence (AI) techniques and algorithms are increasingly being utilized in energy and renewable research to tackle various engineering problems. However, a majority of the AI studies in the energy domain have been focusing on solving specific technical issues. There is limited discussion on how AI can be utilized to enhance the energy system operations, particularly the electricity market, with a holistic view. The purpose of the study is to introduce the platform architectural logic that encompasses both technical and economic perspectives to the development of AI-enabled energy platforms for the future electricity market with massive and distributed renewables. A constructive and inductive approach for theory building is employed for the concept proposition of the AI energy platform by using the aggregated data from a European Union (EU) Horizon 2020 project and a Finnish national innovation project. Our results are presented as a systemic framework and high-level representation of the AI-enabled energy platform design with four integrative layers that could enable not only value provisioning but also value utilization for a distributed energy system and electricity market as the new knowledge and contribution to the extant research. Finally, the study discusses the potential use cases of the AI-enabled energy platform.

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AI Modelling and Time-series Forecasting Systems for Trading Energy Flexibility in Distribution Grids

Cited by 7 publications

References 4 publications

Deployment and Management of Time Series Forecasts in Ocean Industry

Deployment and Management of Time Series Forecasts in Ocean Industry

Knowledge- and Data-driven Services for Energy Systems using Graph Neural Networks

Electricity Market Empowered by Artificial Intelligence: A Platform Approach

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