Shaping the future of energy with multi-microgrids - Techno-economic optimization study
Due to the COVID-19 crisis the PhD defence of Paolo Fracas will take place (partly) online.
The PhD defence can be followed by a live stream.
Paolo Fracas is a PhD student in the research group Sustainable Process Technology (SPT). His supervisors are prof.dr.ir. E. Zondervan and prof.dr.ing. M.B. Franke from the Faculty of Science & Technology (S&T).
The world is currently facing enormous dramatic energy and environmental challenges caused by global warming and increased energy demand. Achieving a low-carbon economy is a priority. The current energy system is economically inefficient to provide a climate-neutral economy and too rigid for massive renewable energy penetration.
Fortunately, cost-effective IT infrastructures and communication technologies, are boosting the connectivity of distributed renewable energy resources into peer-to-peer power systems. A new paradigm of energy produced in small and smart independent energy networks is now emerging, namely the microgrid. This energy system integrates the generation, transmission, distribution, and consumption of energy as a whole, providing an intelligent optimal interaction among all nodes of the systems. The microgrid is a groundbreaking technology that enables scalable and thus, more economic, and affordable financial investments.
A strategic interest of stakeholders in the energy value chain is to choose the optimal combination of technologies, operations, location of power systems that are best suited to provide the necessary energy services at the most economical conditions.
However, finding the best design, operations, and geographic location of a microgrid is a complex task. Novel computer-based optimization techniques are needed to solve this stochastic problem.
The objective of this work lies in the techno-economic optimization of combined heat and power multi-microgrids, a renewable-based distribution system architecture comprising two interconnected microgrids that strengthen the concept of distributed technology with tight and more effective integration of energy devices.
The optimal location, combined design with optimal operation of these hybrid energy systems have only been partially investigated. High computational resources are needed to search for global solutions fitting local climate conditions and energy demand services.
To this aim, a novel two-level optimization framework is discussed to simultaneously search optimal design, sizing, and siting while balancing energy dispatch with minimal operating costs and the highest revenues.
The objective function combines machine learning and techno-economic analytical models of several thermal and electric distributed energy resources.
The results demonstrate that interconnected heat and power microgrids are globally a competitive technology, returning high IRRs of up to 65%, and energy costs of less than €14/kWh even in off-grid contests, making further investment in fossil fuel generation worthless.