Skip to main content

Research Repository

Advanced Search

Dr Behzad Kazemtabrizi's Outputs (59)

An Integrated Stacked Sparse Autoencoder and CNN-BLSTM Model for Ultra-Short-Term Wind Power Forecasting with Advanced Feature Learning (2025)
Presentation / Conference Contribution
Liu, J., Kazemtabrizi, B., Du, H., Matthews, P., & Sun, H. (2024, November). An Integrated Stacked Sparse Autoencoder and CNN-BLSTM Model for Ultra-Short-Term Wind Power Forecasting with Advanced Feature Learning. Presented at 50th Annual Conference of the IEEE Industrial Electronics Society, Chicago, USA

With the increasing integration of renewable energy sources into the power grid, accurate and reliable ultra-short-term forecasting of wind power is critical for optimizing grid stability and energy efficiency, especially for a highly dynamic and var... Read More about An Integrated Stacked Sparse Autoencoder and CNN-BLSTM Model for Ultra-Short-Term Wind Power Forecasting with Advanced Feature Learning.

Data-driven estimation of the amount of under frequency load shedding in small power systems (2024)
Journal Article
Rajabdorri, M., Troffaes, M. C. M., Kazemtabrizi, B., Sarvarizadeh, M., Sigrist, L., & Lobato, E. (2025). Data-driven estimation of the amount of under frequency load shedding in small power systems. Engineering Applications of Artificial Intelligence, 139(Part B), Article 109617. https://doi.org/10.1016/j.engappai.2024.109617

This paper presents a data-driven methodology for estimating under frequency load shedding (UFLS) in small power systems. UFLS plays a vital role in maintaining system stability by shedding load when the frequency drops below a specified threshold fo... Read More about Data-driven estimation of the amount of under frequency load shedding in small power systems.

Minimising the Impact of Contingency in Multiple-Period Short Term Operational Planning with RAS-FUBM For Wind Integration (2024)
Presentation / Conference Contribution
Khadijah Hamzah, S., Kazemtabrizi, B., & Shahbazi, M. (2024, June). Minimising the Impact of Contingency in Multiple-Period Short Term Operational Planning with RAS-FUBM For Wind Integration. Presented at PEMD 2024: Power Electronics, Machines and Drives 13th International Conference, Nottingham, UK

Renewable energy-based power systems are highly influenced by weather, creating uncertainty and variability in output that fluctuates over time, making the balancing of supply and demand much more challenging. A deterministic optimisation tool has li... Read More about Minimising the Impact of Contingency in Multiple-Period Short Term Operational Planning with RAS-FUBM For Wind Integration.

Data-Driven Infrastructure Planning for Offshore Wind Farms (2024)
Presentation / Conference Contribution
Saxena, I., Kazemtabrizi, B., Troffaes, M. C., & Crabtree J., C. (2024, May). Data-Driven Infrastructure Planning for Offshore Wind Farms. Presented at Torque 2024, Florence, Italy

Offshore wind farms are one of the major renewable energy resources that can help the UK to reach its net zero target. Under the 10 point plan of the green revolution, the UK is set to quadruple its wind energy production by increasing its offshore w... Read More about Data-Driven Infrastructure Planning for Offshore Wind Farms.

Network reconfiguration under a stochastic optimisation framework for Day-Ahead Operation Planning for Future Distribution Networks (2023)
Presentation / Conference Contribution
Higuera, G., & Kazemtabrizi, B. (2023, June). Network reconfiguration under a stochastic optimisation framework for Day-Ahead Operation Planning for Future Distribution Networks. Presented at 27th International Conference on Electricity Distribution (CIRED 2023), Rome, Italy

This paper proposes a novel Active Network Management (ANM) framework for day-ahead operations planning of a medium-voltage active Distribution Network (DN) using a heuristic Network Reconfiguration (NR) algorithm with a Curtailment Minimisation Sche... Read More about Network reconfiguration under a stochastic optimisation framework for Day-Ahead Operation Planning for Future Distribution Networks.

Inclusion of frequency nadir constraint in the unit commitment problem of small power systems using machine learning (2023)
Journal Article
Rajabdorri, M., Kazemtabrizi, B., Troffaes, M., Sigrist, L., & Lubato, E. (2023). Inclusion of frequency nadir constraint in the unit commitment problem of small power systems using machine learning. Sustainable Energy, Grids and Networks, 36, Article 101161. https://doi.org/10.1016/j.segan.2023.101161

As the intention is to reduce the amount of thermal generation and to increase the share of clean energy, power systems are increasingly becoming susceptible to frequency instability after outages due to reduced levels of inertia. To address this iss... Read More about Inclusion of frequency nadir constraint in the unit commitment problem of small power systems using machine learning.

Stochastic Optimization of an Active Network Management Scheme for a DER-Rich Distribution Network Comprising Various Aggregators (2023)
Presentation / Conference Contribution
Adeniji, O., & Kazemtabrizi, B. (2023, June). Stochastic Optimization of an Active Network Management Scheme for a DER-Rich Distribution Network Comprising Various Aggregators. Presented at EEEIC2023: 23rd International Conference on Environment and Electrical Engineering, Madrid, Spain

With large-scale acceptance of solar and wind energy generation into electric grids, large energy storage is expected to provide sufficient flexibility for the safe, stable and economic operation of power systems under uncertainty. Active Network Man... Read More about Stochastic Optimization of an Active Network Management Scheme for a DER-Rich Distribution Network Comprising Various Aggregators.

Optimum operational planning of wind-integrated power systems with embedded Multi-terminal High Voltage Direct Current Links using the Flexible Universal Branch Model (2023)
Presentation / Conference Contribution
Hamzah, S. K., & Kazemtabrizi, B. (2023, June). Optimum operational planning of wind-integrated power systems with embedded Multi-terminal High Voltage Direct Current Links using the Flexible Universal Branch Model. Presented at EEEIC2023: 23rd International Conference on Environment and Electrical Engineering, Madrid, Spain

This paper presents a comprehensive analysis of a meshed MT-HVDC topology with three wind farms connected to the Substation Ring Topology (SRT) and investigates the steady state performance of the MT-HVDC system based on the VSC control strategies (i... Read More about Optimum operational planning of wind-integrated power systems with embedded Multi-terminal High Voltage Direct Current Links using the Flexible Universal Branch Model.

Multivariate CNN-LSTM model for wind power forecast and input variables correlation analysis based on SHAPLEY values (2023)
Presentation / Conference Contribution
Pina-Gongora, D. C., & Kazemtabrizi, B. (2023, May). Multivariate CNN-LSTM model for wind power forecast and input variables correlation analysis based on SHAPLEY values. Paper presented at Wind Energy Science Conference (WESC) 2023, Glasgow, UK

Wind Power forecast based on the use of modern Deep Neural Network (DNN) methods is being explored as a tool to support decision-making by wind farm developers and operators for investment, grid integration and operational planning of the site. Techn... Read More about Multivariate CNN-LSTM model for wind power forecast and input variables correlation analysis based on SHAPLEY values.

Calculating the Maximum Penetration of Electric Vehicles in Distribution Networks with Renewable Energy and V2G (2023)
Presentation / Conference Contribution
Thomas, H., Sun, H., & Kazemtabrizi, B. (2023, March). Calculating the Maximum Penetration of Electric Vehicles in Distribution Networks with Renewable Energy and V2G. Presented at ISGT ME 2023, Abu Dhabi, UAE

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.

Performance comparison of Probabilistic and Artificial Neural Network Models for Long-sequence Generation of Wind Speed Forecasts (2022)
Presentation / Conference Contribution
Pina-Gongora, D. C., Kazemtabrizi, B., & Crabtree, C. (2022, October). Performance comparison of Probabilistic and Artificial Neural Network Models for Long-sequence Generation of Wind Speed Forecasts. Presented at 21st Wind and Solar Integration Workshop (WIW 2022), The Hague, Netherlands

This paper presents a new method for generating long-sequence wind speed time-series forecasts for purposes of offshore wind farm asset and operations planning. Our goal is to develop a planning decision support tool with which wind farm planners and... Read More about Performance comparison of Probabilistic and Artificial Neural Network Models for Long-sequence Generation of Wind Speed Forecasts.

Real-time resilient Microgrid power management based on multi-agent systems with price forecast (2022)
Journal Article
Cruz-Victorio, M., Kazemtabrizi, B., & Shahbazi, M. (2023). Real-time resilient Microgrid power management based on multi-agent systems with price forecast. IET Smart Grid, 6(2), 190-204. https://doi.org/10.1049/stg2.12089

Microgrids have emerged to diversify conventional electric generation using small-scale distributed generation. Large efforts have been put into designing control strategies to optimise the power schedules of microgrids, however, verification that su... Read More about Real-time resilient Microgrid power management based on multi-agent systems with price forecast.

Statistical Evaluation of Wind Speed Forecast Models for Microgrid Distributed Control (2022)
Journal Article
Cruz Victorio, M. E., Kazemtabrizi, B., & Shahbazi, M. (2022). Statistical Evaluation of Wind Speed Forecast Models for Microgrid Distributed Control. IET Smart Grid, 5(5), 347-362. https://doi.org/10.1049/stg2.12073

With the increasing needs to decarbonise existing energy systems, there is an effort to integrate small-scale distributed generation sources, such as wind generators, with the electric demand in circuits known as microgrids. The operation of distribu... Read More about Statistical Evaluation of Wind Speed Forecast Models for Microgrid Distributed Control.

Inclusion of Frequency Stability Constraints in Unit Commitment Using Separable Programming (2021)
Journal Article
Ferrandon-Cervantes, C., Kazemtabrizi, B., & Troffaes, M. (2022). Inclusion of Frequency Stability Constraints in Unit Commitment Using Separable Programming. Electric Power Systems Research, 203, Article 107669. https://doi.org/10.1016/j.epsr.2021.107669

In this paper we address the problem of frequency stability in the unit commitment (UC) optimisation process. We include a set of appropriately defined frequency stability constraints in the UC problem formulation for operational planning scenarios i... Read More about Inclusion of Frequency Stability Constraints in Unit Commitment Using Separable Programming.

Price Forecast Methodologies Comparison for Microgrid Control with Multi-Agent Systems (2021)
Presentation / Conference Contribution
Cruz Victorio, M., Kazemtabrizi, B., & Shahbazi, M. (2023, June). Price Forecast Methodologies Comparison for Microgrid Control with Multi-Agent Systems. Presented at 14th IEEE PES PowerTech Conference, Madrid, Spain

Multi-Agent systems offer a way to control distributed generation in microgrids, reliability and cost minimisation capabilities can be improved by price forecast methodologies that can be deployed without the need of external control signals. This pa... Read More about Price Forecast Methodologies Comparison for Microgrid Control with Multi-Agent Systems.

Closest Energy Matching: Improving peer‐to‐peer energy trading auctions for EV owners (2021)
Journal Article
Thomas, H., Sun, H., & Kazemtabrizi, B. (2021). Closest Energy Matching: Improving peer‐to‐peer energy trading auctions for EV owners. IET Smart Grid, 4(4), 445-460. https://doi.org/10.1049/stg2.12016

Herein, a novel approach to conduct peer-to-peer energy auctions for electric vehicles (EVs) to benefit both buyers and sellers is presented. It considers a scenario where households can sell their surplus solar energy to visiting EVs that make use o... Read More about Closest Energy Matching: Improving peer‐to‐peer energy trading auctions for EV owners.

Integrated condition-based maintenance modelling and optimisation for offshore wind farms (2021)
Journal Article
Dao, C. D., Kazemtabrizi, B., Crabtree, C. J., & Tavner, P. J. (2021). Integrated condition-based maintenance modelling and optimisation for offshore wind farms. Wind Energy, 24(11), 1180-1198. https://doi.org/10.1002/we.2625

Maintenance is essential in keeping wind energy assets operating efficiently. With the development of advanced condition monitoring, diagnostics and prognostics, condition‐based maintenance has attracted much attention in the offshore wind industry i... Read More about Integrated condition-based maintenance modelling and optimisation for offshore wind farms.

Universal Branch Model for the Solution of Optimal Power Flows in Hybrid AC/DC Grids (2020)
Journal Article
Alvarez-Bustos, A., Kazemtabrizi, B., Shahbazi, M., & Acha, E. (2021). Universal Branch Model for the Solution of Optimal Power Flows in Hybrid AC/DC Grids. International Journal of Electrical Power & Energy Systems, 126(Part A), Article 106543. https://doi.org/10.1016/j.ijepes.2020.106543

This paper presents a universal model formulation for solving Optimal Power Flows for hybrid AC/DC grids. The prowess of the new formulation is that it (i) provides a direct link between AC and DC parts of the grid allowing for solving the entire net... Read More about Universal Branch Model for the Solution of Optimal Power Flows in Hybrid AC/DC Grids.