Optimization-based design tools for energy systems often require a large set of parameter assumptions, e.g., about technology efficiencies and costs or the temporal availability of variable renewable energies. Understanding the influence of all these parameters on the computed energy system design via direct sensitivity analysis is not easy for human decision-makers, since they may become overloaded by the multitude of possible results. We thus propose transferring an approach from explaining complex neural networks, so-called locally interpretable model-agnostic explanations (LIME), to this related problem. Specifically, we use variations of a small number of interpretable, high-level parameter features and sparse linear regression to obtain the most important local explanations for a selected design quantity. For a small bottom-up optimization model of a grid-connected building with photovoltaics, we derive intuitive explanations for the optimal battery capacity in terms of different cloud characteristics. For a larger application, namely a national model of the German energy transition until 2050, we relate path dependencies of the electrification of the heating and transport sector to the correlation measures between renewables and thermal loads. Compared to direct sensitivity analysis, the derived explanations are more compact and robust and thus more interpretable for human decision-makers.
This paper focuses on the steady-state analysis of location and control of distributed power generators encompassing a virtual power plant (VPP). The significant increase worldwide in distributed generation from renewable energy has been largely due to the concern with the environment, the grid access and the strong support policies for the promotion of renewable electricity. Deployment of distributed power plants has contributed to the new concept of VPP as a solution to handle the mix of conventional and distributed energy sources. Produced energy from even small scale generators will be utilized in energy markets and can contribute to the enhancement of the electrical power system. When aggregated, the group of DG would gain access and visibility across the energy markets and the system operation will benefit from optimal use of all available capacity and increased efficiency of operation.
We present an easily accessible model for dispatch and expansion planning of the German multi-modal energy system from today until 2050. The model can be used with low efforts while comparing favorably with historic data and other studies of future developments. More specifically, the model is based on a linear programming partial equilibrium framework and uses a compact set of technologies to ease the comprehension for new modelers. It contains all equations and parameters needed, with the data sources and model assumptions documented in detail. All code and data are openly accessible and usable. The model can reproduce today’s energy mix and its CO2 emissions with deviations below 10%. The generated energy transition path, for an 80% CO2 reduction scenario until 2050, is consistent with leading studies on this topic. Our work thus summarizes the key insights of previous works and can serve as a validated and ready-to-use platform for other modelers to examine additional hypotheses.
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