Building as Flexibility Nodes - Modeling for Grid and System Operation

As the global energy landscape undergoes a transformative shift towards intermittent renewable sources, the need for reliable and economically efficient energy matching has become increasingly crucial. The traditional utilities and distribution operators are adapting to evolving business models, emphasizing the demand side flexibility for sustainable energy solutions. This master thesis addresses this paradigm shift by investigating the role of buildings as pivotal flexibility nodes and models a simulation environment for the grid studies of the Energy Communities (EC), particularly from the perspective of grid operators. The primary objective of this study is to develop a Python-based Buildings Flexibility Model (BFM) tailored for simulations of smart grid/smart building flexibility use cases, including self-consumption, self-sufficiency, peak-shaving and load-scheduling. By exploring various impacts on the grid through rule-based controller during the demand-side flexibility provision, the research aims to enhance the understanding of the dynamic interactions between buildings and the broader energy system. The study rigorously analyzes the consequences on the grid of households participating in Demand Response (DR) programs. It specifically compares elements such as self-consumption, self-sufficiency, and active power exchange at the Point of Common Coupling (PCC). The emphasis is on contrasting prosumeroriented behavior with DR mechanisms, particularly peak-shaving and load-scheduling. The model incorporates essential variables such as household priorities, weather dependency and DR participation shares to provide a comprehensive understanding of those diverse factors influencing flexibility. This research contributes significant insights to the modern energy system landscape by offering a comprehensive understanding of building flexibility through an innovative simulation approach.