Solar-Powered Green Hydrogen MATLAB Simulation

echnical Project Brief: Solar-Powered Green Hydrogen via PEM Electrolysis (condensed)

1. Description
Develop a complete, reproducible MATLAB simulation assessing the technical performance and economic feasibility of green hydrogen from solar PV electricity and a PEM electrolyser in London, UK. Entirely computer-based (no hardware, lab work or purchases). Combine real solar data, mathematical modelling, energy-performance calculations, economic evaluation and sensitivity analysis. All numerical results must come from the working model, not be invented or manually estimated without justification.
Software: MATLAB. Location: London. Preferred data: EC JRC PVGIS. Period: 2019–2023, hourly where available. Energy: solar PV only. Electrolysis: PEM.

2. Aim and objectives
Aim: evaluate technical performance and economic feasibility of solar-powered PEM electrolysis under London conditions.
Objectives: (1) obtain and analyse historical solar data; (2) model PV generation; (3) develop a PEM electrolyser model; (4) integrate both in MATLAB; (5) calculate hourly, monthly and annual electricity generation and hydrogen production; (6) evaluate specific electricity consumption, efficiency, energy losses and system utilisation; (7) estimate LCOH; (8) run sensitivity analysis on key technical and economic parameters; (9) verify the model and identify limitations.

3. Data collection
Use PVGIS (https://re.jrc.ec.europa.eu/pvg_tools/en/): genuine London data, 2019–2023, hourly where available and appropriate. Document location, dataset, retrieval settings, timestamps, irradiance variables, units, missing values and processing methods. Keep the original downloads and the processed dataset. Do not fabricate measurements; if data are unavailable, explain the limitation before choosing an alternative. Irradiance must suit the PV model: do not confuse horizontal with tilted-array irradiance without conversion or justification.

4. System configuration
Irradiance → PV electricity → electrolyser → hydrogen → technical and economic evaluation. PV is the only generation; no wind, batteries or grid in the baseline. Propose and justify baseline PV and electrolyser capacities using engineering principles and reliable sources; investigate alternatives through a limited number of scenarios. Document the system boundary and the treatment of auxiliary electricity, cooling, water treatment, compression, storage and other balance-of-plant; acknowledge excluded components as limitations.

5. PV model
Starting approximation: P_PV(t) = P_rated × [G(t)/G_STC] × f_T(t) × f_loss, where G_STC = 1,000 W/m², f_T = temperature effects (where modelled), f_loss = documented system losses. Not mandatory: choose a method suited to the data and explain its assumptions. Document capacity, orientation, module parameters, conversion efficiency, losses and temperature corrections. Keep units consistent and outputs physically reasonable.

6. PEM electrolyser model
Simplified: m_H2(t) = E_el(t)/SEC_H2(t) (m in kg, E in kWh, SEC in kWh/kg H2). Use parameters from credible literature or published equipment specifications; distinguish published values from assumptions. Account for rated power, consumption, production, SEC, efficiency and minimum operating power where data allow. A detailed current–voltage/Faraday's-law model is allowed if justified; avoid unnecessary complexity and unsupported parameters. State whether efficiency is constant or load-dependent, and how partial load, auxiliary consumption and operating limits are handled.

7. Integrated simulation
Consistent hourly time step. Each step: read solar data; compute PV power and available energy; determine electricity available to the electrolyser; apply operating limits; compute consumption and hydrogen production; compute unused/curtailed PV energy; store outputs. Electrolyser must not exceed rated power; hydrogen is zero when not operating; do not assume surplus can be stored or used later (no battery). Produce hourly outputs and aggregate to monthly and annual summaries. Report individual-year results separately from five-year totals.

8. Technical performance indicators (define each and its system boundary)
Total PV generation; electrolyser consumption; unused/curtailed PV; hourly, monthly, annual hydrogen; SEC (kWh/kg H2); electrolyser efficiency; equivalent full-load hours; capacity factor (with definition); % of PV electricity consumed by the electrolyser; seasonal changes in performance and utilisation.
Efficiency = (H2 mass × heating value) / electricity consumed, using a consistent convention (LHV ≈ 33.3 kWh/kg H2). Distinguish electrolyser efficiency from overall PV-to-hydrogen efficiency.

9. Economic assessment
LCOH in GBP/kg H2. Costs: PV capex; PEM capex; balance of plant; O&M; stack replacement; degradation; project lifetime and discount rate; auxiliary energy and other operating costs within the boundary. Use credible sources, document assumptions, and test uncertain values by sensitivity analysis.
LCOH = present value of lifetime costs / present value of lifetime hydrogen production. Explain treatment of capital, operating and replacement costs, discounting and annual production. Avoid double-counting electricity when PV electricity is already in PV capex/opex. NPV and payback are optional, only with a defensible revenue or cash-flow scenario.

10. Sensitivity analysis
Vary: PEM efficiency/SEC; PV capacity; electrolyser capacity; PV and electrolyser capex; O&M; stack lifetime and replacement cost; discount rate and lifetime; PV losses; solar-resource variability. Define a justified baseline and reasonable alternatives from reliable sources or stated assumptions. Start one-at-a-time; if practical add a few combined scenarios. Present tables and graphs; a tornado chart of influence on LCOH is useful. All results must be calculated by the model.

11. Verification and QA
Verify: timestamps and units consistent; missing/invalid data handled transparently; hydrogen non-negative and zero when not operating; electrolyser power ≤ rated; energy and mass conversions correct; PV generation reconciles with consumption + unused energy; logical behaviour at zero, low and high irradiance; parameter changes give explainable results. Compare with published specifications or peer-reviewed research where appropriate. Distinguish code verification from real-world validation; claim no experimental validation without experimental evidence.

12. Required graphs (from actual model outputs; labelled axes, units, legends, scenario descriptions)
(1) Monthly solar-resource profile; (2) representative hourly PV power profiles; (3) monthly PV generation; (4) monthly hydrogen production; (5) annual hydrogen by year; (6) PV generation vs electrolyser demand; (7) annual energy balance; (8) seasonal performance comparison; (9) LCOH cost breakdown; (10) LCOH sensitivity; (11) comparison of alternative capacities, if investigated.

13. Deliverables

• MATLAB code: complete scripts/functions; main simulation script; integrated PV and PEM models; documented inputs and assumptions; technical and economic calculations; sensitivity routines; graph scripts.
• Data/results: original solar data (where redistribution is permitted); processed inputs; hourly outputs; monthly and annual summaries; CSV or Excel-compatible exports.
• Assumptions register: for each parameter, value, unit, source, published vs assumed, sensitivity range.
• Technical documentation: equations, model structure, data sources, boundaries, assumptions, calculation sequence, energy balance, metrics, economic method, sensitivity analysis, verification, limitations.
• Reproducibility: run instructions, software requirements, dependencies; confirm the code reproduces the reported outputs.

14. Working arrangements
Before implementation, give a short methodology proposal: PV model, PEM model, baseline capacities, data processing, economic assumptions, timeline, and any extra information needed. Prioritise technical accuracy, traceable data, reproducible calculations and clear explanations over complexity. Do not fabricate data, references, specifications or results; distinguish assumptions from measured and published values. Discuss scope changes before implementing.
Final objective: a working, reproducible MATLAB simulation of solar-powered PEM hydrogen production in London, with technical evaluation, LCOH estimation and sensitivity analysis.