Topic title: Multi-objective optimization for the design of Smart PV-modules
Photovoltaic (PV) modules yield considerable lower energy in the field than what is expected from their rated power, indicated as "Watt-peak (Wp)". The latter is measured under "standard test conditions" but in climates like Western Europe, these are rarely met. In fact, the main energy yield losses (kWh/kWp) in these climates, can be attributed to a reduced illumination resulting in lower current, and non-uniform illumination conditions (shading, clouds, soiling, ...) leading to current mismatch in the serially connected cells inside the module.
Partial shading especially represents one of the major source of losses. It is foreseen for the future that operation of PV modules under partial shading will become more and more common, given the trends toward Near Zero-energy Buildings and solar-powered vehicles. As an example, building integration of PV modules means that they are installed on different surfaces of the building, e.g. different facades, so that close-by buildings, street poles and other obstacles typical of an urban environment will often lead to partial shading.
In order to maximize the power production of PV modules working under non-uniform illumination conditions, we are building advanced smart PV modules able to dynamically establish different non-series topologies. These modules are organized in small groups of cells (cell-strings) that can be connected each other either in series or in parallel by means of reconfiguration switches. Intra-module (local) converters are used to ensure suitable current and voltage level and allow a direct control of the operating point of the cell-string.
The design of such smart PV modules is a non-trivial multi-objective optimization process. The main goal of smart PV modules is to improve performance under partial shading. To reach this goal, many different approaches must be considered and combined: using smaller cells, such half-cell and quarter-cell, to reduce the current flowing through the module thus the resistive losses; increasing the number of cell-strings within a module in order to allow for a higher granularity; defining the number of local converters and how they are connected both to cell-strings and each other. However, such design procedure cannot focus only on better performance under partial shading. Indeed, improved partial shading resiliency comes often with higher losses under uniform conditions due to both non-unitary efficiency of power converters and higher resistive losses related to additional electrical components such as the reconfiguration switches. To reduce such losses while keeping good performance under shading, different interconnection schemes for both cells and converters, as well as concepts like differential and partial power processing must be considered. Also, a proper selection of the PV module topology must take into account the cost increase related to the number and type of module-integrated components as local converters, reconfiguration switches and material for additional wiring. Moreover, the position of such additional module-integrated components within the PV module itself affects both module performance and complexity of the manufacturing process.
In this topic, we want to develop such a multi-objective optimization procedure to design smart PV modules. Given the importance of proper models of the additional module-integrated components, the candidate will collaborate with researchers in the area of power electronics and electronic components design. On a practical level for implementation, the conceived solutions have to take into account the boundary conditions imposed by the PV module technology (in parallel under development at imec), to allow seamless integration. Apart from devising the concept and fabrication of proof-of-concept demo modules, it is of course important to characterize the modules under controlled conditions, relevant to realistic outdoor operation of PV modules.
Required background: Master in Science, Master in Engineering
Type of work: 15% literature study + 65% modeling/algorithm development + 20% experimental
Supervisor: Francky Catthoor, Jef Poortmans
Daily advisor: Patrizio Manganiello
The reference code for this position is 1812-75. Mention this reference code on your application form.