I want a clear, data-driven picture of how a power system performs when it is heavy on renewables yet faced with unpredictable demand. My primary interest lies in wind energy, but I also plan to integrate a commercial-scale solar PV array whose main purpose is to drive down electricity costs. The job is to build (or adapt) a simulation that captures: • Hour-to-hour variability of a sizeable wind fleet • Output and cost impact of a commercial rooftop or ground-mount PV system • Stochastic or scenario-based load profiles that reflect real-world uncertainty I am comfortable with Python (PyPSA, Pandapower), MATLAB/Simulink, or DIgSILENT PowerFactory—use whatever you feel delivers the most transparent and reproducible results. I will provide resource data and basic load traces; you turn them into a working model, run the key scenarios, and present the findings in a concise report with the underlying code/notebooks. Acceptance criteria 1. Executable model files with clear documentation and comments 2. A brief report (≈10 pages) summarising methodology, assumptions, and cost/energy results for each scenario 3. Graphs that highlight system balance, curtailment, and savings achieved through the solar addition If this scope is clear and the tools align with your expertise, let’s discuss timelines and milestones so we can move straight into the modelling work.