• Energy Research

For an extensive decarbonization of district multi-energy systems, efforts are needed that go beyond today's cogeneration of heat and power in district multi-energy systems. The multitude of existing technical possibilities are confronted with a large variety of existing multi-energy system configurations. The variety impedes the development of universal decarbonization pathways. In order to tackle the decarbonization challenge in existing and distinct districts, this paper calculates a wide range of urban district configurations in an extensive co-simulation based on domain specific submodels. A district multi-energy system comprising a district heating network, a power grid, and cogeneration is simulated for two locations in Germany with locally captured weather data, and for a whole year with variable parameters to configure a power-to-heat operation, building insolation/refurbishment, rooftop photovoltaic orientation, future energy demand scenarios, and district sizes with a temporal resolution of 60 seconds, in total 3840 variants. The interdependencies and synergies between the electrical low-voltage distribution grid and the district heating network are analysed in terms of efficiency and compliance with network restrictions. Thus, important sector-specific simulations of the heat and the electricity sector are combined in a holistic district multi-energy system co-simulation. The clearly most important impact on emission reduction and fuel consumption is a low heat demand, which can be achieved through thermal refurbishment of buildings. Up to $\SI{46}{\percent}$ reduction in $CO_2$ emissions are possible using the surplus electricity from photovoltaics for power-to-heat in combination with central heat storage in the district's combined heat and power plant. Domestic hot water heated by district heating network in combination with power-to-heat conversion distributed in the district reduces the load on the distribution power grid. Even though the investigated measures already improve the sustainability significantly, providing the energy needed for the production of synthetic fuels remains the crucial challenge on the further path towards net-zero.

Existing open-source modeling frameworks dedicated to energy systems optimization typically utilize (mixed-integer) linear programming ((MI)LP) formulations, which lack modeling freedom for technical system design and operation. We present COMANDO, an open-source Python package for component-oriented modeling and optimization for nonlinear design and operation of integrated energy systems. COMANDO allows to assemble system models from component models including nonlinear, dynamic and discrete characteristics. Based on a single system model, different deterministic and stochastic problem formulations can be obtained by varying objective function and underlying data, and by applying automatic or manual reformulations. The flexible open-source implementation allows for the integration of customized routines required to solve challenging problems, e.g., initialization, problem decomposition, or sequential solution strategies. We demonstrate features of COMANDO via case studies, including automated linearization, dynamic optimization, stochastic programming, and the use of nonlinear artificial neural networks as surrogate models in a reduced-space formulation for deterministic global optimization. 24 pages, 1 graphical abstract, 13 figures, 4 tables