Supervisor:
Tamas Kerekes

Co-Supervisor:
Zhongting Tang

Assessment Committee:
Erik Schaltz (Chair)
Olav Bjarte Fosso. Dept. of Electric Power Engineering, NTNU
Rosa Anna Mastromauro. Dept. of Information Engineering, University of Florence

Moderator:
Daniel Ioan-Stroe

Abstract:

Photovoltaic (PV) energy has proliferated in the last few years and reshaped power systems. Nevertheless, with the increasing penetration of solar energy, the mismatch and uncertainties of PV energy have challenged the economical and reliable operation of the power grid. Energy storage system (ESS) integration into the PV system is seen as a promising solution to the above problems, but the control of the hybrid PV-ESS systems still needs more research. Therefore, the subject of this thesis is to develop practical energy and power management strategies for hybrid PV-ESS systems in different operation scenarios. The contribution of this thesis is divided into two parts: (1) energy and power management strategies for residential applications. (2) energy management strategies for utility grids according to the grid code.

A contribution of this thesis is to develop energy management strategies for residential applications. PV and battery ESS are considered for current residential cases considering the installation investments and volume constraints. Then, based on the system configuration and mission profiles, the commonly used energy management strategies are reviewed and compared regarding the economics and sensitivity. Furthermore, an energy management strategy based on the artificial potential field (APF) is proposed, which outperforms competitive rule-based strategies.

Another contribution is to develop some energy management strategies to achieve specific grid functions. In this thesis, active power grid functions of ramp rate constraint, and frequency regulation are considered. In this context, the necessity of hybrid ESS (HESS) is discussed at the beginning, and a sizing method is proposed for the PV-HESS system to achieve the most cost-effectiveness. Then, for the ramp rate constraint, two adaptive low-pass filter (LPF)-based strategies are proposed. Specifically, one strategy adjusts the LPF time constants according to the battery state of the charge (SOC), hence reducing the battery burden to extend its lifetime. Another strategy adopts the APF method to regulate the LPF time constant to reduce the HESS capacity and extend the battery lifetime. Finally, for the frequency regulation function, a near-optimal energy management strategy is proposed for a grid-forming PV-HESS system. By optimizing the power allocation offline in different operation scenarios, three-segment rules can be extracted accordingly. And combining the deep learning-based prediction method, the extracted rules can achieve near-optimal control, hence reducing battery degradation compared with competitive rule-based methods.