PhD opportunities

Operations and control of hybrid hydro power stations with battery energy storage systems for future sustainable power systems

Thesis proposal

Area of expertiseEnergy and Processes
Doctoral SchoolSystems Engineering, Materials, Mechanics, Energy
SupervisorM. Georges KARINIOTAKIS
Research unitEnergy and Processes
Starting dateOctober 1st 2019
KeywordsHydropower plants, battery systems, predictive control, model predictive control, optimization, renewable energies
AbstractBackground:

Hydropower plants are important players of the European electricity landscape and, in 2017, accounted for 15% of the total generated electricity. Although they generally have quicker ramping capabilities than coal and nuclear power plants (15% of nominal capacity per minute vs 5%), their setup needs to be adapted to meet the increasing demand for fast regulation required to accommodate larger shares of production from renewables. Moreover, the decreasing price differentials observed in wholesale energy markets weaken the business case of hydro-pumping stations, requiring their operators to enter fast flexibility markets to retain profitability. The concept of 'hybrid hydropower plants' refers to augment conventional hydropower units with a parallel grid-connected battery energy storage system (BESS) with the objectives of increasing its response time to time-varying power set-points and reducing the wear and tear of the hydraulic components. Generally, grid-connected BESS, thanks to storing energy in a quickly reversible electrochemical reaction and being connected with power converters, can implement active and reactive power set-points in a much faster way than hydropower units. This, in confluence with the decreasing prices, makes of lithium-ion BESSs an extremely appealing option for hydropower plants operators to increase the flexibility of their asset. This problematic has been recognized as a priority at the European level, resulting in a new research project granted under the LC-SC3-RES-17-2019 call that is the framework of this Ph.D. project.

Scientific objectives:

The objective is developing, validating and testing advanced control algorithms for hybrid variable speed hydropower stations. As a function of fast varying (i.e., second and sub-second time scale) power set-points, the algorithms should determine feasible control trajectories for both the battery and hydropower unit. These trajectories should respect the operational constraints of the two types of units while minimizing the electrochemical aging of the battery and wear and tear processes of the hydro turbine. The proposed control algorithms will be validated in real-life in collaboration with the laboratory of hydraulic machines (LMH, https://lmh.epfl.ch) and of distributed electrical systems (DESL, https://desl-pwrs.epfl.ch) of the Swiss Federal Institute of Technology of Lausanne (EPFL), that hosts 3x300 kW hydraulic machine experimental test-rigs and a 720~kVA/560~MWh. For this reason, external research stays at those laboratories are foreseen.

Methodology and expected results:

Control algorithms will leverage reduced order models of the hydro turbine (e.g., Hill chart) and battery (e.g., equivalent circuit model, aging models) to determine the power set-points of the battery and hydropower generating unit as a function of an external control signal, based on, e.g., (but not limited to) model predictive control. Algorithms will be initially developed and validated in a simulation context and will be subsequently engineered to achieve industrial-grade reliability in order to perform the required lab-scale tests. Due to the need for testing the algorithm in real-life, tractability will be a key attribute of the developed algorithms, thus they will leverage efficient computational models.
ProfileFor applying: All interest candidates with suitable background are encouraged to apply online by filling the form at https://forms.gle/YZJMDihDV75TrhsW7 and uploading their CV, cover letter, and recommendation letters (if available).
FundingContrat de recherche