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Calculating the Maximum Penetration of Electric Vehicles in Distribution Networks with Renewable Energy and V2G (2023)
Conference Proceeding
Thomas, H., Sun, H., & Kazemtabrizi, B. (2023). Calculating the Maximum Penetration of Electric Vehicles in Distribution Networks with Renewable Energy and V2G. In 2023 IEEE PES Conference on Innovative Smart Grid Technologies - Middle East (ISGT Middle East). https://doi.org/10.1109/isgtmiddleeast56437.2023.10078520

The uptake of electric vehicles and distributed energy generation is adding significant new demand to distribution networks, however it is unknown whether this can be accommodated by existing infrastructure. This paper first presents an Optimisation... Read More about Calculating the Maximum Penetration of Electric Vehicles in Distribution Networks with Renewable Energy and V2G.

Digital Twins for Smart Cities: Case Study and Visualisation via Mixed Reality (2022)
Conference Proceeding
Piper, W., Sun, H., & Jiang, J. (2022). Digital Twins for Smart Cities: Case Study and Visualisation via Mixed Reality. . https://doi.org/10.1109/vtc2022-fall57202.2022.10012753

Digital twins is an increasingly valuable technology for realising smart cities worldwide. Visualising this technology using mixed reality creates unprecedented opportunities to easily access relevant data and information. In this paper, a digital tw... Read More about Digital Twins for Smart Cities: Case Study and Visualisation via Mixed Reality.

Electric Vehicle Battery Pack Design for Mitigating Thermal Runaway Propagation (2022)
Conference Proceeding
Copsey, E., Sun, H., & Jiang, J. (2022). Electric Vehicle Battery Pack Design for Mitigating Thermal Runaway Propagation. . https://doi.org/10.1109/vtc2022-fall57202.2022.10012777

The production of electric vehicle battery packs with ever-increasing energy densities has accelerated the electrification of the world’s automotive industry. With increased attention on the electric vehicle markets, it is vital to increase the safet... Read More about Electric Vehicle Battery Pack Design for Mitigating Thermal Runaway Propagation.

A Microgrid Management System Based on Metaheuristics Particle Swarm Optimization (2022)
Conference Proceeding
Deng, H., Jiang, J., Qian, H., & Sun, H. (2022). A Microgrid Management System Based on Metaheuristics Particle Swarm Optimization. . https://doi.org/10.1109/icsgsc56353.2022.9963000

Microgrid is playing an increasingly important role in making the utility grid more intelligent and efficient, since it can make better use of the renewable energy resources to simultaneously relieve the grid supply pressure and reduce carbon emissio... Read More about A Microgrid Management System Based on Metaheuristics Particle Swarm Optimization.

Demand side management considering household appliances and EV (2022)
Conference Proceeding
Dong, Z., Jiang, J., Qian, H., & Sun, H. (2022). Demand side management considering household appliances and EV. . https://doi.org/10.1109/icsgsc56353.2022.9963032

Combination of the information technology and the power engineering is the feature of next-generation grid. Depending on bidirectional communications, demand side management (DSM) aims at optimizing the electricity usage pattern of customers to impro... Read More about Demand side management considering household appliances and EV.

Protecting privacy in microgrids using federated learning and deep reinforcement learning (2022)
Conference Proceeding
Chen, W., Sun, H., Jiang, J., You, M., & Piper, W. (2022). Protecting privacy in microgrids using federated learning and deep reinforcement learning. . https://doi.org/10.1049/icp.2023.0100

This paper aims to improve the energy management efficiency of home microgrids while preserving privacy. The proposed microgrid model includes energy storage systems, PV panels, loads, and the connection to the main grid. A federated multi-objective... Read More about Protecting privacy in microgrids using federated learning and deep reinforcement learning.

Appliance Scheduling Optimisation Method Using Historical Data in Households with RES Generation and Battery Storage Systems (2022)
Conference Proceeding
Correa-Delval, M., Sun, H., Matthews, P. C., & Chiu, W. (2022). Appliance Scheduling Optimisation Method Using Historical Data in Households with RES Generation and Battery Storage Systems. . https://doi.org/10.1109/repe55559.2022.9949497

In recent years, the importance of reducing carbon dioxide (CO2) emissions has increased. With the use of technologies such as artificial intelligence, we can improve the way households manage their energy use to decrease cost and carbon emissions. I... Read More about Appliance Scheduling Optimisation Method Using Historical Data in Households with RES Generation and Battery Storage Systems.

Statistical Power Grid Observability under Finite Blocklength (2022)
Conference Proceeding
Zhan, Q., Liu, N., Pan, Z., & Sun, H. (2022). Statistical Power Grid Observability under Finite Blocklength. . https://doi.org/10.1109/cniot55862.2022.00026

We study the stochastic observability of the power grid system under communication constraints in the finite blocklength regime. Compared to the study under the assumption of infinite blocklength, we introduce two new elements: probability of decodin... Read More about Statistical Power Grid Observability under Finite Blocklength.

Statistical Power Grid Observability under NOMA-based Communication Constraints (2022)
Conference Proceeding
Zhan, Q., Liu, N., Pan, Z., & Sun, H. (2022). Statistical Power Grid Observability under NOMA-based Communication Constraints. . https://doi.org/10.1109/cniot55862.2022.00017

This paper studies the observability of the power grid by jointly considering the power system with the wireless communication system under the strict latency requirements of Phasor Measurement Units (PMUs), which is characterized via the theory of e... Read More about Statistical Power Grid Observability under NOMA-based Communication Constraints.

Short-term load forecasting using artificial neural networks and social media data (2021)
Conference Proceeding
Boyd, A., Sun, H., Black, M., & Jesson, S. (2021). Short-term load forecasting using artificial neural networks and social media data. . https://doi.org/10.1049/icp.2021.1552

Evolving practices around energy generation, storage and trading within the UK have made it more necessary than ever to provide accurate means of forecasting electricity demand. This paper considers deep neural networks with convolutional and recurre... Read More about Short-term load forecasting using artificial neural networks and social media data.

Blockchain Smart Contracts for Grid Connection Management in Achieving Net Zero Energy Systems (2021)
Conference Proceeding
Hua, W., Jing, R., Zhou, Y., Zhang, X., Jiang, J., & Sun, H. (2022). Blockchain Smart Contracts for Grid Connection Management in Achieving Net Zero Energy Systems. . https://doi.org/10.46855/energy-proceedings-9190

Energy systems are transitioning towards a decentralized and decarbonized paradigm with the integration of distributed renewable energy sources. Blockchain smart contracts have the increasing potential to facilitate the transition of energy systems d... Read More about Blockchain Smart Contracts for Grid Connection Management in Achieving Net Zero Energy Systems.

Appliance Classification using BiLSTM Neural Networks and Feature Extraction (2021)
Conference Proceeding
Correa-Delval, M., Sun, H., Matthews, P., & Jiang, J. (2021). Appliance Classification using BiLSTM Neural Networks and Feature Extraction. . https://doi.org/10.1109/isgteurope52324.2021.9640061

One significant challenge in Non-Intrusive Load Monitoring (NILM) is to identify and classify active appliances used in a building. This research focuses on the classifying process, exploring different approaches for the feature extraction of the app... Read More about Appliance Classification using BiLSTM Neural Networks and Feature Extraction.

Reinforcement Learning Based Load Balancing for Geographically Distributed Data centres (2021)
Conference Proceeding
Mackie, M., Sun, H., & Jiang, J. (2021). Reinforcement Learning Based Load Balancing for Geographically Distributed Data centres.

This paper proposes a method of migrating workload among geo-distributed data centres that are equipped with on-site renewable energy sources (RES), such as solar and wind energy, to decarbonise data centres. It aims to optimise the performance of su... Read More about Reinforcement Learning Based Load Balancing for Geographically Distributed Data centres.

A Data Augmentation based DNN Approach for Outage-Constrained Robust Beamforming (2021)
Conference Proceeding
You, M., Zheng, G., & Sun, H. (2021). A Data Augmentation based DNN Approach for Outage-Constrained Robust Beamforming. . https://doi.org/10.1109/icc42927.2021.9500736

This paper studies the long-standing problem of outage-constrained robust downlink beamforming in the multiuser multi-antenna wireless communications systems. State of the art solutions have very high computational complexity which poses a major chal... Read More about A Data Augmentation based DNN Approach for Outage-Constrained Robust Beamforming.

Trust-based Model for Securing Vehicular Networks Against RSU Attacks (2021)
Conference Proceeding
Alnasser, A., & Sun, H. (2021). Trust-based Model for Securing Vehicular Networks Against RSU Attacks. . https://doi.org/10.1109/infocomwkshps51825.2021.9484458

Intelligent Transportation System (ITS) is one of the Internet of Things (IoT) systems that can achieve reliable transportation by providing communications between vehicles and infrastructure units. The interaction between them is called Vehicle-to-E... Read More about Trust-based Model for Securing Vehicular Networks Against RSU Attacks.

Data-driven Pricing and Control for Low Carbon V2G Charging Station with Balancing Services (2020)
Conference Proceeding
Hernandez Cedillo, M., & Sun, H. (2020). Data-driven Pricing and Control for Low Carbon V2G Charging Station with Balancing Services. . https://doi.org/10.1109/smartgridcomm47815.2020.9303018

The transition to a low carbon transportation system has brought many challenges for researchers, one major challenge is how to ensure power system reliability as a result of high load demands to supply energy to Electric Vehicles (EVs) while coping... Read More about Data-driven Pricing and Control for Low Carbon V2G Charging Station with Balancing Services.

An Internet of Things (IoT) Homecare Management System Using Cardiac Arrhythmia Classification (2020)
Conference Proceeding
Almazroa, A., & Sun, H. (2021). An Internet of Things (IoT) Homecare Management System Using Cardiac Arrhythmia Classification. . https://doi.org/10.1109/ntms49979.2021.9432672

Due to the fast growing of population, a lot of hospitals get crawdad from the huge amount of patients visits. The need for providing patient care while they are at home at anytime is important and the era of homecare should started. Internet of Thin... Read More about An Internet of Things (IoT) Homecare Management System Using Cardiac Arrhythmia Classification.

Multi-Metric QoS-balancing Relay Selection Algorithm in V2X Communications (2019)
Conference Proceeding
Alnasser, A., Sun, H., & Jiang, J. (2019). Multi-Metric QoS-balancing Relay Selection Algorithm in V2X Communications. In Proceedings of the IEEE Global Communications Conference (Globecom) Workshops 2019 (1-6). https://doi.org/10.1109/gcwkshps45667.2019.9024498

As the network topology of the Vehicle-to-Everything (V2X) frequently changes, direct communication with the infrastructure unit is not always available. Therefore, the road entity requires to choose Device-to-Device (D2D) relay node to forward its p... Read More about Multi-Metric QoS-balancing Relay Selection Algorithm in V2X Communications.