Sarma, Debopama Sen2024-12-032024-12-032024http://hdl.handle.net/2003/4300210.17877/DE290R-24835The ongoing energy transition has led to a paradigm shift in distribution power systems infrastructure and operations owing to the ever-increasing volume of intermittent renewable energy generation and electricity consumption across several recently developed sectors. Integrating previously separated energy sectors and electricity market liberalization transforms the distribution system environment into a complex system that includes several participants with conflicting interests. Therefore, innovative approaches to optimal and efficient energy management of available resources are paramount to achieving sustainability goals through economic, environmental, social, and technical factors. This dissertation presents an agent-based hierarchical energy management architecture that draws from research and practically applies to the liberalized electricity market. The architecture is designed to solve a multi-objective optimization routine for distribution grid operation in a distributed way. An online feedback mechanism is a vital feature of the optimization algorithm, which is solved in collaboration with the agents representing the various participants in a distribution grid ecosystem. A unique combination of Tchebycheff's decomposition and the Gradient projection method is used to solve the constrained and multi-objective optimal power flow problem. Adding penalty functions to the objective function effectively handles the system constraints. The formulation of industrial and residential demand profiles incorporates socio-behavioral aspects of the connected prosumers, reflecting their economic, behavioral, or environmental goals. The energy management architecture and its optimization function are then applied in a co-simulation framework to study the impact of industrial and residential flexibility on the connected distribution grid's economic, environmental, and technical aspects. The results provide valuable indicators for distribution grid planning and operation under future renewable generation and flexible load integration scenarios.en620PhDThesisEnergieversorgungsnetz