3. Enhanced PV Systems

Increasingly, PV is just a part of a more complex energy system within a house, building or district, which performs multiple energy services (e.g. heating, air conditioning, energy storage, electric vehicle charging, load shifting) as well as participating as a prosumer in the electricity markets. Such systems need to be optimised whether they are purpose-built or retrofitted, to interact positively with the economic and environmental perspectives of the building e.g. energy certification. SOLVE will provide research on optimal system design and real time, data-driven management to maximise economic return, to control power distribution, to couple with battery/hydrogen storages and to perform usage/production predictions for the end user. Such outputs can underpin new system-level solutions for improved functionality, flexibility, transparency and economic return for the prosumer.

Theme Leaders

Senior Lecturer/Associate Professor

Uppsala University

Professor Energy Engineering

Dalarna University

Projects in Theme 3

A joint project of Theme3. Enhanced PV systems and Theme 6. Planning for large-scale expansion

A joint project of Theme3. Enhanced PV systems and Theme 6. Planning for large-scale expansion

The project is about developing mathematical, statistical and machine learning methods to optimize the use of solar power in combination with e.g. battery storage, charging of electric vehicles, heat pump systems and ancillary services to the electricity grid. In addition to this, the work involves developing and applying methods for optimal location and sizing of solar parks, given boundary conditions such as an alternative land use and the hosting capacity of the electricity distribution network.

We study how small-scale solar electricity generation can be integrated with the local energy systems at urban district/neighbourhood level using energy sharing between buildings and the electricity grid, as well as with other neighbourhoods.

The PhD candidate will work on developing mathematical, statistical and machine learning methods for optimizing large-scale solar power utilization in local energy systems and electricity grids.

The thesis work will evaluate the accuracy of predictions of solar irradiance and thus PV output on a meteorological day-ahead forecast. The aim is to optimize earnings by maximizing the FCR-D downwards bid.

Region Uppsala, Region Dalarna, CTEK, Glava Energy Centre, CheckWatt, Vattenfall, Svenska kyrkan, Air by Solar, Kraftpojkarna

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