Jonathan F.

Energy Modeller | Energy Systems Optimisation & Techno-Economic Analysis I’m an Energy Systems Modeller who designs and builds clear, transparent models that help utilities, developers, IPPs, investors, city authorities, and industrial sites make better energy decisions. With 6+ years’ experience and a First-Class MEng in Chemical Engineering (University of Birmingham), I use Python (PyPSA/Pyomo), PLEXOS, MATLAB and Excel to answer practical questions. The outputs support strategic investment and day-to-day operations across generation, transmission, and distribution. Here are some of my key strengths and skills summarised: Advanced energy systems modelling and optimisation Techno-economic analysis (LCOE, LCOM, LCOH, NPV, IRR) Excel‑based financial modelling: integrated P&L/Cash‑flow/Balance‑sheet models, project‑finance structures, valuation, scenario & sensitivity analysis Data analysis, process simulation, and energy demand forecasting Project management and stakeholder collaboration Tools: Python (pandas, NumPy, PyPSA, Pyomo), PLEXOS, MATLAB, Excel, TensorFlow Selected past work: GB dispatch & stack modelling: Built a transparent zonal DC dispatch with merit-order stacks and price-duration views to show asset position and curtailment risk. Wind co-location: battery sizing for NPV: Chose MW/MWh to maximise NPV under losses, degradation, and grid limits; delivered clear size/cost/revenue trade-offs. Battery arbitrage + ancillary co-optimisation: Single-shot LP selecting reserve vs arbitrage with ramps, spreads, and SoC deliverability; transparent profit split. Stranded-energy monetisation: Compared BTC miners vs a small BESS using surplus profiles; recommended scale and triggers based on risk-adjusted NPV. EV charging scheduler (chance-constrained): Rolling-horizon optimiser that meets SoC-by-departure targets without breaching feeder limits; optional V2G. Capacity-expansion planning: 15–20-year roadmap for least-cost build/retire with adequacy and CO₂ constraints; auditable assumptions and scenarios. Synthetic methane study: LCOM/LCOH modelling to inform site selection and economics for an e-methane plant. Hub-and-spoke siting optimisation: Python model to select manufacturing hubs under cost, capacity, and logistics constraints. City net-zero pathway: Decarbonisation baselines and options assessment with clear KPIs for policy and investment planning. Utility tariff review: Built a dynamic Excel model for scenario testing and comparative tariff analysis. BESS/V2G research: Techno-economic analysis with demand/price forecasting to guide storage sizing and market participation.