Jobid=95ed2a2aeed1 (0.0144)
ph3Job description /h3 pThe overarching goal of the SPROUT project is to enable a large scale transition of the Dutch greenhouse horticulture sector toward renewable integrated, highly flexible energy hubs that can actively support the national electricity grid. Greenhouse companies currently provide a significant share of the Netherlands’ dispatchable power capacity; as they move away from fossil fuel based CHP units, the wider energy system risks losing a crucial flexibility resource. Through strategic integration hybrid energy storage systems with greenhouse operations via optimization methods, control architectures, and scalable system designs, this project aims to ensure that greenhouse energy systems can continue to provide grid supporting flexibility. These greenhouse energy hubs can thereby reduce the need for expensive new dispatchable generation capacity, enable deeper renewable energy penetration, mitigate grid congestion, and reducing CO₂ emissions at national scale. /p pIn this PhD project, you will develop advanced predictive control methods to optimize the operation of an integrated system of hybrid energy storage systems (i.e., multi-carrier energy hub or micro-grid) and high-tech greenhouses. These hubs combine electricity, heat, hydrogen, CO₂, and other energy carriers with greenhouse climate control, resulting in a highly coupled, nonlinear, multi-timescale dynamical system. Your work will focus on building dynamic system models for both the energy conversion technologies and the greenhouse climate, integrating these into a unified framework suitable for state estimation, predictive control, and flexibility optimization. /p pA key challenge is to quantify and exploit the inherent operational flexibility of greenhouse processes—such as lighting, heating, and CO₂ dosing—while respecting crop physiology constraints and ensuring reliable operation under uncertainty, variable energy prices, and grid balancing needs. You will develop hierarchical and/or integrated predictive control strategies that coordinate energy hub assets with greenhouse climate actuators, exploring multiple operational objectives: cost minimization, flexibility maximization, and load shifting in response to grid signals. The project will require translating high fidelity physical/physiological models into computationally efficient control oriented models, supported by online parameter estimation to ensure adaptability across greenhouse types. /p pYour algorithms will first be evaluated through simulation using real operational datasets, and later deployed and tested at two physical facilities: a kW scale testbed at TU Delft’s Green Village and a MW scale greenhouse hub operated by Division Q. /p pThis PhD position is well suited for candidates eager to work at the intersection of nonlinear systems, optimal control, and energy systems engineering, with real‑world impact and experimental validation. You will collaborate closely with industrial partners (eFuelution, Division Q) and interdisciplinary researchers at TU Delft (TPM, energy system modeling and optimization) and Wageningen University and Research (plant physiology), contributing directly to the Dutch horticulture sector’s transition toward more flexible, renewable integrated operation. /p h3Teaching activities /h3 pTeaching activities are part of your PhD trajectory and may include, for example: supervising workgroups or lab sessions, assisting in courses, or mentoring BSc and MSc students. While teaching will not be your main responsibility, it offers valuable experience that supports your development and prepares you for future academic or professional roles. Teaching activities will not exceed 20% of your total appointment, averaged over the course of your PhD. /p h3Job requirements /h3 ul liCompleted a relevant MSc degree in systems and control, engineering, applied mathematics, or a related field. /li liA sufficient background and strong interest in systems and control, model predictive control, and energy systems. /li liSome experience in basic software engineering and implementation of algorithms in real‑world experiments or industrial practice is a plus, but not required. /li liExperience with greenhouse climate control is not required, but an interest in this aspect of the research is important. /li /ul h3Faculty Mechanical Engineering /h3 pResearch and education at the Faculty of Mechanical Engineering focuses on fundamental understanding, design, production including application and product improvement, materials, processes and (mechanical) systems. The faculty offers high‑tech lab facilities and an international research environment. /p h3Conditions of employment /h3 pDoctoral candidates will be offered a 4‑year period of employment in principle, but in the form of 2 employment contracts. An initial 1.5 year contract with an official go/no‑go progress assessment within 15 months. Followed by an additional contract for the remaining 2.5 years assuming everything goes well and performance requirements are met. /p pAs a PhD candidate you will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment with an excellent team of supervisors, academic staff and a mentor. The Doctoral Education Programme is aimed at developing your transferable, discipline‑related and research skills. /p pThe TU Delft offers a customisable compensation package, discounts on health insurance, and a monthly work costs contribution. Flexible work schedules can be arranged. /p /p #J-18808-Ljbffr
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