n this article, an open-source expansion planning tool for energy hubs, based on the Python library PyPSA, is presented. The expansion planning optimization uses an operational optimization based on hourly timeseries. The energy hub model contains detailed energy generation, conversion, conditioning, and storage components for electricity, heat, and various chemicals, including CH4, H2, and CO2. For heat, multiple temperature levels are considered. As an example, the tool is used to optimize the expansion planning for a Waste-to-Energy CHP plant and district heating (DH) network in Germany. Techno-economical conditions for 2023 and projected conditions for 2050 are used as inputs. Results are analysed using timeseries and frequency analysis. This provides insight in how each planned component should be used to optimize revenues. The analyses show the importance of using hourly timeseries as input, as certain processes are operated in close alignment with periodic fluctuations in electricity prices or heat demand. Parameters are varied to evaluate the robustness of the investment decisions. The optimization results show that the studied CHP plant could transform to an energy hub, benefiting from synergetic effects when coupling multiple energy sectors using Power-to-X technologies. The tool can be used to assist operators in their investment planning to shape the future of CHP sites in Europe.