Towards Cyber Security for Low-Carbon Transportation: Overview, Challenges and Future Directions (2023)
Journal Article
Cao, Y., Li, S., Lv, C., Wang, D., Sun, H., Jiang, J., …Cheng, X. (2023). Towards Cyber Security for Low-Carbon Transportation: Overview, Challenges and Future Directions. Renewable and Sustainable Energy Reviews, 183, Article 113401. https://doi.org/10.1016/j.rser.2023.113401

In recent years, low-carbon transportation has become an indispensable part as sustainable development strategies of various countries, and plays a very important responsibility in promoting low-carbon cities. However, the security of low-carbon tran... Read More about Towards Cyber Security for Low-Carbon Transportation: Overview, Challenges and Future Directions.

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.

Transactive Energy and Flexibility Provision in Multi-microgrids using Stackelberg Game (2022)
Journal Article
Hua, W., Xiao, H., Pei, W., Chiu, W., Jiang, J., Sun, H., & Matthews, P. (2023). Transactive Energy and Flexibility Provision in Multi-microgrids using Stackelberg Game. CSEE journal of power and energy systems, 9(2), 505-515. https://doi.org/10.17775/cseejpes.2021.04370

Aggregating the demand side flexibility is essential to complementing the inflexible and variable renewable energy supply in achieving low carbon energy systems. Sources of demand side flexibility, e.g., dispatchable generators, storages, and flexibl... Read More about Transactive Energy and Flexibility Provision in Multi-microgrids using Stackelberg Game.

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.

Dynamic Pricing and Control for EV Charging Stations with Solar Generation (2022)
Journal Article
Hernandez Cedillo, M., Sun, H., Jiang, J., & Cao, Y. (2022). Dynamic Pricing and Control for EV Charging Stations with Solar Generation. Applied Energy, 326, Article 119920. https://doi.org/10.1016/j.apenergy.2022.119920

Demand response is one of the most promising tools for smart grids to integrate more renewable energy sources. One critical challenge to overcome is how to establish pricing and control strategies for integrating more electric vehicles (EVs) and rene... Read More about Dynamic Pricing and Control for EV Charging Stations with Solar Generation.

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.

A novel deep learning based peer‐to‐peer transaction method for prosumers under two‐stage market environment (2022)
Journal Article
Peng, D., Xiao, H., Pei, W., Sun, H., & Ye, S. (2022). A novel deep learning based peer‐to‐peer transaction method for prosumers under two‐stage market environment. IET Smart Grid, 5(6), 430-439. https://doi.org/10.1049/stg2.12078

With the development of the electricity market, peer-to-peer (P2P) transaction plays an important role in promoting local consumption of renewable energy and arousing the enthusiasm of prosumers. However, due to the diversification of prosumers, the... Read More about A novel deep learning based peer‐to‐peer transaction method for prosumers under two‐stage market environment.

Applications of blockchain and artificial intelligence technologies for enabling prosumers in smart grids: A review (2022)
Journal Article
Hua, W., Chen, Y., Qadrdan, M., Jiang, J., Sun, H., & Wu, J. (2022). Applications of blockchain and artificial intelligence technologies for enabling prosumers in smart grids: A review. Renewable and Sustainable Energy Reviews, 161, Article 112308. https://doi.org/10.1016/j.rser.2022.112308

Governments’ net zero emission target aims at increasing the share of renewable energy sources as well as influencing the behaviours of consumers to support the cost-effective balancing of energy supply and demand. These will be achieved by the advan... Read More about Applications of blockchain and artificial intelligence technologies for enabling prosumers in smart grids: A review.

Data-driven prosumer-centric energy scheduling using convolutional neural networks (2022)
Journal Article
Hua, W., Jiang, J., Sun, H., Tonello, A., Qadrdan, M., & Wu, J. (2022). Data-driven prosumer-centric energy scheduling using convolutional neural networks. Applied Energy, 308, Article 118361. https://doi.org/10.1016/j.apenergy.2021.118361

The emerging role of energy prosumers (both producers and consumers) enables a more flexible and localised structure of energy markets. However, it leads to challenges for the energy scheduling of individual prosumers in terms of identifying idiosync... Read More about Data-driven prosumer-centric energy scheduling using convolutional neural networks.

Consumer-centric decarbonization framework using Stackelberg game and Blockchain (2022)
Journal Article
Hua, W., Jiang, J., Sun, H., Teng, F., & Strbac, G. (2022). Consumer-centric decarbonization framework using Stackelberg game and Blockchain. Applied Energy, 309, Article 118384. https://doi.org/10.1016/j.apenergy.2021.118384

Energy policy is too often not designed for energy consumers in a low-cost and consumer-friendly manner. This paper proposes a novel Stackelberg game and Blockchain-based framework that enables consumer-centric decarbonization by automating iterative... Read More about Consumer-centric decarbonization framework using Stackelberg game and Blockchain.

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.

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.

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.