Energy hub optimization framework based on open-source software & data - review of frameworks and a concept for districts & industrial parks

Multi-model energy systems are gaining importance in a world where different types of energy, such as electricity, natural gas, hydrogen, and hot water, are used to create more complex but also more economic energy systems to support deep decarbonization.  While the research community is using open source for a long-time collaborative work on open-source tools is not yet the norm within the research community.  To increase the open and sharing efforts between research organizations governments are driving publicly funded projects to share their outcomes.  The proposed open-source framework is based on the principle of maximizing the reuse of existing data, software snippets and packages, and add individual code only as necessary.  A screening of more than hundred software packages identified six suitable open-source frameworks to be partly incorporated into the new open-source framework.  The best parts of each of these frameworks are combined in a way that utilizes limited human resources in an optimal way.  To further improve the so created energy system framework additional features such as a scenery model to incorporate shadowing and elevation effects on conventional and renewable power generation technologies are included.  Going forward, this approach allows to expand research into urban air assessment in which traffic and energy emissions can be assessed jointly

Modeling of Thermal Storage Systems in MILP Distributed Energy Resource Models,

Thermal energy storage (TES) and distributed generation technologies, such as combined heat and power (CHP) or photovoltaics (PV), can be used to reduce energy costs and decrease CO2 emissions from buildings by shifting energy consumption to times with less emissions and/or lower energy prices. To determine the feasibility of investing in TES in combination with other distributed energy resources (DER), mixed integer linear programming (MILP) can be used. Such a MILP model is the well-established Distributed Energy Resources Customer Adoption Model (DER-CAM); however, it currently uses only a simplified TES model to guarantee linearity and short run-times. Loss calculations are based only on the energy contained in the storage. This paper presents a new DER-CAM TES model that allows improved tracking of losses based on ambient and storage temperatures, and compares results with the previous version. A multi-layer TES model is introduced that retains linearity and avoids creating an endogenous optimization problem. The improved model increases the accuracy of the estimated storage losses and enables use of heat pumps for low temperature storage charging. Results indicate that the previous model overestimates the attractiveness of TES investments for cases without possibility to invest in heat pumps and underestimates it for some locations when heat pumps are allowed. Despite a variation in optimal technology selection between the two models, the objective function value stays quite stable, illustrating the complexity of optimal DER sizing problems in buildings and microgrids