The global energy system is undergoing a major transition, and in energy planning and decision-making across governments, industry and academia, models play a crucial role. Because of their policy relevance and contested nature, the transparency and open availability of energy models and data are of particular importance. Here we provide a practical how-to guide based on the collective experience of members of the Open Energy Modelling Initiative (Openmod). We discuss key steps to consider when opening code and data, including determining intellectual property ownership, choosing a licence and appropriate modelling languages, distributing code and data, and providing support and building communities. After illustrating these decisions with examples and lessons learned from the community, we conclude that even though individual researchers' choices are important, institutional changes are still also necessary for more openness and transparency in energy research.Comment: 9 pages, 1 figur

API changes Allow conversion factor of zero for GenericTransformer Python 3.6 is no longer officially supported. It may still work for a while though. New components/constraints Custom component: oemof.solph.custom.PiecewiseLinearTransformer. A transformer model with one input and one output and an arbitrary piecewise linear conversion function. On how to use the component, refer to the test script _ and example _. Enhanced custom SinkDSM: Renamed keyword argument method to approach Renamed approaches interval to oemof and delay to DIW Added modeling approach DLR (PhD thesis of Hans Christian Gils 2015) Included load shedding Introduced recovery_time in DIW approach Introduced shift_time and other parameters for DLR approach Included investments in DSM Normalized keyword arguments demand, capapcity_up and capacity_down Bug fixes Check number of Flow s in GenericStorage Other changes Split code into submodules Move CI-Tests from Travis to github (see PR #746 _) Contributors Jann Launer Johannes Kochems Patrik Schönfeldt Stefan Schirmeister Uwe Krien

In conventional energy system planning, cost optimisation is usually the decisive factor. The objective of the research, on which this article is based, is to develop alternatives to cost-optimised energy supply concepts that are near optimal cost and also meet the criterion of increased resilience. The methodology presented here thus expands the solution space for the planning of energy systems and the consideration of additional criteria beyond pure cost optimisation. The transition from a fossil fuel-based energy system to one reliant on renewable sources brings significant structural changes and uncertainties. Resilience management offers a guiding concept to address the non-linear complexities and unpredictability of this transformation process and to cope with uncertain and unknown stressors. Thus, a comparative assessment of the resilience of different future energy concepts is crucial to provide a basis for decision making and implementation of resilient energy systems. This research approach entailed the optimisation of a heat supply concept for an urban district and the investigation of near-optimal alternatives in the vicinity of the optimal solution. The resilience of these near-optimal solutions was then analysed. For this purpose, certain resilience-enhancing structures and functionalities (diversity, redundancy, buffer capacity) were evaluated by quantifiable indicators. The analysis of the heat supply scenarios has shown that resilience, measured by the indicators used, could be increased at a low additional cost. In the top-performing alternative-heat-supply scenarios generated, the diversity has been increased by 585%, redundancy by 18% and buffer capacity by 98%. The majority of the generated alternatives that were examined showed that an increase in diversity and redundancy could be achieved at a relatively low additional cost.