Data-driven modelling of energy demand response behaviour based on a large-scale residential trial

Recent years have seen an increasing interest in Demand Response(DR),as a means to satisfy the growing flexibility needs of modern power grids.This increased flexibility is required due to the growing proportion of intermittent renewable energy generation into the energy mix,and increasing complexity in demand profiles from the electrification of transport networks.Currently,less than 2%of the global potential for demand-side flexibility is currently utilised,but a more widespread adoption of residential consumers as flexibility resources can lead to substantially higher utilisation of the demand-side flexibility potential.In order to achieve this target,acquiring a better understanding of how residential DR participants respond in DR events is essential–and recent advances in novel machine learning and statistical AI provide promising tools to address this challenge.This study provides an in-depth analysis of how residential customers have responded in incentive-based DR,utilising household-related data from a large-scale,real-world trial:the Smart Grid,Smart City(SGSC)project.Using a number of different machine learning approaches,we model the relationship between a household’s response and household-related features.Moreover,we examine the potential effects of households’features on the residential response behaviour,and highlight a number of key insights which raise questions about the reported level of consumers’engagement in DR schemes,and the motivation for different customers’response level.Finally,we explore the temporal structure of the response–and although we found no supporting evidence of DR responders learning over time for the available data from this trial,the proposed methodologies could be used for longer-term longitudinal DR studies.Our study concludes with a broader discussion of our findings and potential paths for future research in this emerging area.

A review: Challenges and opportunities for artificial intelligence and robotics in the offshore wind sector

The UK has set plans to increase offshore wind capacity from 22GW to 154GW by 2030. With such tremendous growth, the sector is now looking to Robotics and Artificial Intelligence (RAI) in order to tackle lifecycle service barriers as to support sustainable and profitable offshore wind energy production. Today, RAI applications are predominately being used to support short term objectives in operation and maintenance. However, moving forward, RAI has the potential to play a critical role throughout the full lifecycle of offshore wind infrastructure, from surveying, planning, design, logistics, operational support, training and decommissioning. This paper presents one of the first systematic reviews of RAI for the offshore renewable energy sector. The state-of-the-art in RAI is analyzed with respect to offshore energy requirements, from both industry and academia, in terms of current and future requirements. Our review also includes a detailed evaluation of investment, regulation and skills development required to support the adoption of RAI. The key trends identified through a detailed analysis of patent and academic publication databases provide insights to barriers such as certification of autonomous platforms for safety compliance and reliability, the need for digital architectures for scalability in autonomous fleets, adaptive mission planning for resilient resident operations and optimization of human machine interaction for trusted partnerships between people and autonomous assistants. Our study concludes with identification of technological priorities and outlines their integration into a new ‘symbiotic digital architecture’ to deliver the future of offshore wind farm lifecycle management.

Prediction of voltage distribution using deep learning and identified key smart meter locations

The energy landscape for the Low-Voltage(LV)networks is undergoing rapid changes.These changes are driven by the increased penetration of distributed Low Carbon Technologies,both on the generation side(i.e.adoption of micro-renewables)and demand side(i.e.electric vehicle charging).The previously passive‘fit-and-forget’approach to LV network management is becoming increasing inefficient to ensure its effective operation.A more agile approach to operation and planning is needed,that includes pro-active prediction and mitigation of risks to local sub-networks(such as risk of voltage deviations out of legal limits).The mass rollout of smart meters(SMs)and advances in metering infrastructure holds the promise for smarter network management.However,many of the proposed methods require full observability,yet the expectation of being able to collect complete,error free data from every smart meter is unrealistic in operational reality.Furthermore,the smart meter(SM)roll-out has encountered significant issues,with the current voluntary nature of installation in the UK and in many other countries resulting in low-likelihood of full SM coverage for all LV networks.Even with a comprehensive SM roll-out privacy restrictions,constrain data availability from meters.To address these issues,this paper proposes the use of a Deep Learning Neural Network architecture to predict the voltage distribution with partial SM coverage on actual network operator LV circuits.The results show that SM measurements from key locations are sufficient for effective prediction of the voltage distribution,even without the use of the high granularity personal power demand data from individual customers.