Dr Evgenia Mechleri is a Lecturer in the Department of Chemical and Process Engineering at the University of Surrey. Prior to this, she was a Research Fellow in the Energy Institute at Brunel University at the RCUK National Centre for Sustainable Energy Use in Food Chain. She has worked on the “OPTEMIN project: Optimising Energy Management in Industry”, an EPSRC funded project which is taking a whole systems approach to the optimisation of energy management in industry, with a view to meeting long-term targets for reducing greenhouse gas emissions and global warming. She has also worked as a Research Associate for four years at Imperial College London in the Department of Chemical Engineering and the Centre for Environmental Policy. She was involved with various projects related to multi-scale systems modelling, covering the areas of decision making, control and optimisation. Her research is focused on developing and applying decision-making tools and control strategies designed for environmental and cost-effective issues in energy planning processes. She has also established and rigorously evaluated control methodologies for the analysis and optimal control and scheduling of dynamic systems.
- Grid scale energy storage
- Multi-scale energy systems modelling
- Photovoltaic systems
- Global Solar Irradiance
- Renewable Energy Sources
- Distributed Energy Systems
- Model Predictive Control (MPC)
- Optimisation and Design of Systems and Processes
- Carbon Capture and Storage (CCS)
Design Projects ENG3192
Engineering systems and dynamics ENG2120 (Module leader)
Transferable and laboratory skills ENG1083 (Module leader)
Supervision of MEng Research Projects ENGM276
Supervision of MSc Research Dissertation Projects ENGM083
This study will focus on the development of an integrated IoT-Distributed energy systems (DES) model for the efficient energy management of a microgrid under the integration of the intermittent renewable energy resources. In this work, we expand the definition of flexible options to include demand and supply together with design and operation strategies using internet of things (IoT). Our framework brings weather data and sensor information into a virtual energy plant optimisation model that connects supplier and consumer to optimise potential flexibility gaps arising from supply and demand mismatch. The problem is posed as a hybrid mixed-integer linear programming (MILP) optimisation model combining flexibility analysis and optimal synthesis for integrating energy supply and demand, where environmental information is added to each stage. Finally, we combine traditional mathematical programming approaches such as flexibility analysis and optimal network synthesis and within a single optimisation framework combining IoT and urban DES.