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Wind energy microgrids in Egypt see 12% cost cut with V2G
Estimated reading time: 6 minutes · Last updated:
A modelling study applied to an off-grid rural site in Egypt shows that combining wind energy, solar PV, batteries, hydrogen storage and vehicle-to-grid (V2G) operation can lower operating costs and improve reliability. Using a double-layer optimisation the authors report a 12% reduction in total operating cost when both EVs and hydrogen vehicles (HVs) operate in G2V and V2G modes, and that imposing a loss-of-power-supply probability (LPSP) limit of 0.05 reduces annualized total cost by about 21.2% versus the unconstrained baseline. The case study is El-Kharga Oasis and the simulated community includes 240 apartments across 12 buildings with 20 EVs and 10 Toyota Mirai HVs.
Key takeaways
- Cost savings from V2G: Applying G2V and V2G for both EVs and HVs delivered a 12% reduction in total operational cost versus the no-V2G scenario.
- Reliability constraint effect: Imposing an LPSP constraint of 0.05 reduced the annualized total cost by around 21.2% compared with the baseline without that constraint.
- Case study scale: The El-Kharga Oasis microgrid model supplies 240 apartments in 12 residential buildings and assumes 20 EVs plus 10 HVs operating within the compound.
- National context: Egypt's Integrated Sustainable Energy Strategy 2035 targets 42% of generation from renewables and the country has signalled green hydrogen ambitions for 2030 and 2040.
Table of contents
- Key takeaways
- What the model tested and the headline results
- How EVs and hydrogen vehicles are modelled and controlled
- El-Kharga Oasis case: resources, users and component behaviour
- Why this matters for Egypt's wind and hydrogen plans
- Paths that would strengthen or limit adoption
- What to be careful about
- Frequently asked questions
What the model tested and the headline results
The paper builds a hybrid renewable microgrid combining photovoltaic arrays, wind turbines, a battery bank and a hydrogen energy storage system made of electrolyser(s), hydrogen tank(s) and fuel cell(s). It links that stationary plant to mobile assets: 20 private electric vehicles (EVs) and 10 hydrogen vehicles (HVs) assigned to on-site managers. The authors deploy a double-layer optimisation: an upper layer that schedules bidirectional vehicle charging and discharging using the NSGA-II genetic algorithm, and a lower layer that sizes components and minimises life-cycle cost using the Gurobi solver (version 12.0.1) called from MATLAB 2022b.
Across scenarios the study reports two headline effects: coordinated G2V/V2G for both EVs and HVs lowers operating cost by 12% against a scenario without V2G, and adding an LPSP constraint of 0.05 cuts the annualized total cost (ATC) by roughly 21.2% relative to the unconstrained baseline. Those outcomes are obtained for an islanded microgrid simulated over three representative months that capture seasonal variation.
How EVs and hydrogen vehicles are modelled and controlled
The vehicle models treat EVs and HVs as stochastic resources. EV daily driving distance, home arrival and departure times are sampled from log-normal and normal probability density functions; the study assumes 20 private EVs charging at home through bidirectional meters. HVs (10 units, modelled as Toyota Mirai) are represented by normal PDFs for arrival time and an initial state-of-charge at the refuelling station. The paper assumes fast hydrogen refuelling of about 3–5 minutes for HVs and constrains V2G discharge from an HV to 10% of its stored energy to reflect fuel-cell cooling limits.
The upper-layer optimisation chooses initial charging/discharging times for each EV and the station status for each HV, producing coupling variables (EV power, HV energy and HV V2G injections) fed to the lower layer. NSGA-II parameters are reported as a population of 200, 500 iterations, 0.9 crossover probability and 0.1 mutation probability. The lower layer then fixes vehicle schedules and optimises component counts and capacities to minimise ATC and LPSP.
El-Kharga Oasis case: resources, users and component behaviour
El-Kharga Oasis (25.441188° N, 30.521838° E) is used as the off-grid testbed because of moderate wind and strong solar potential. Resource time series for three representative months (transition, summer, winter) feed the PV and wind models; the paper cites the Global Solar Atlas 2.0 and the Global Wind Atlas version 4.0 for resource maps. Residential demand comes from 240 apartments in 12 buildings; 30 of those apartments are reserved for managers who are assigned the vehicles.
Stationary-storage behaviour is detailed: battery state-of-charge is bounded and the authors include a small standby loss (reported as 0.006%). Hydrogen storage is modelled with an SOH state updated from electrolyser and fuel-cell flows; the electrolyser minimum power is set to 10% of its maximum. The interaction of hydrogen production, fuel provision for HVs and fuel-cell electricity for the microgrid is central to reducing renewable curtailment and improving reliability in the simulations.
Why this matters for Egypt's wind and hydrogen plans
The study links a local system design to national targets: Egypt's Integrated Sustainable Energy Strategy 2035 aims for 42% of electricity from renewables, and the country has announced green hydrogen export targets of capturing 5% of the global hydrogen market by 2030 and 8% by 2040. A microgrid architecture that couples wind, PV, batteries, hydrogen storage and bidirectional vehicle services could lower local supply costs while supporting those larger goals.
The modelling shows concrete levers for policy and investors: enabling V2G from both EVs and HVs raises the value of flexible demand, and an explicit reliability constraint (LPSP = 0.05) materially reduces lifecycle cost. For developers this implies that investment cases for hydrogen infrastructure and V2G-capable chargers should be evaluated together rather than in isolation.
| Item | Value (from study) | Where reported |
|---|---|---|
| Community size | 240 apartments; 12 buildings | Case study description |
| Vehicles | 20 EVs; 10 HVs (Toyota Mirai) | Residential load / HV model |
| V2G effect on operating cost | 12% reduction versus no-V2G | Abstract / Results |
| LPSP constraint effect | ATC ≈ 21.2% lower with LPSP = 0.05 | Abstract / Results |
| Optimisers | NSGA-II (upper layer); Gurobi 12.0.1 with MATLAB 2022b (lower) | Problem formulation |
Paths that would strengthen or limit adoption
The case for
- If vehicle fleets scale beyond the study's 20 EVs and 10 HVs, additional flexible capacity could further reduce installed stationary capacity and life-cycle cost.
- Lowering electrolyser and fuel-cell capital costs or improving their efficiency would increase the economic case for hydrogen co-storage and fuel provision to HVs.
The case against
- High up-front capital costs for hydrogen infrastructure and uncertainty over domestic HV uptake could delay the realisation of the simulated 12% operating-cost gain.
- If real vehicle arrival/departure behaviour departs from the probability distributions used in the study, the scheduling benefits achieved by NSGA-II may shrink and require recalibration.
What to be careful about
- Model results depend on stochastic assumptions for driving distance and arrival/departure times; real-world deviations could reduce V2G value.
- Hydrogen station assumptions — 3–5 minute refuelling and the 10% V2G discharge cap tied to fuel-cell cooling — affect station throughput and the extent to which HVs can supply grid power.
- Representative-month sampling (three months) reduces computation but may miss extreme resource or demand episodes that change optimal sizing.
The bottom line
The study demonstrates that integrating wind and solar with batteries, hydrogen energy storage and bidirectional vehicle charging can improve reliability and reduce lifecycle costs in an off-grid Egyptian microgrid. Its double-layer method links short-term V2G scheduling (NSGA-II) to long-term sizing (Gurobi 12.0.1 in MATLAB 2022b) and quantifies two practical outcomes: a 12% operating-cost saving from coordinated V2G use and a further ATC reduction of about 21.2% when an LPSP = 0.05 constraint is enforced. For policy and project design this implies that hydrogen infrastructure and V2G-capable charging should be evaluated together when planning wind-driven rural microgrids.
What to watch
- watch for progress on Egypt's Integrated Sustainable Energy Strategy 2035 and delivery toward the 42% renewables target by 2035; the study situates its design against that national goal.
- watch for developments tied to Egypt's green hydrogen ambitions for 2030 (5% global market capture) and 2040 (8% capture), which would affect HV availability and refuelling infrastructure demand.
- watch for local pilot deployments of combined H2ESS and V2G in rural microgrids; no specific deployment date is set in the paper.
Frequently asked questions
How much did V2G operation reduce operating costs in the study?
Coordinating G2V and V2G for both EVs and HVs reduced total operational cost by 12% compared with a scenario without V2G, according to the paper's simulations.
What effect did a reliability constraint have on lifecycle cost?
Imposing a loss-of-power-supply probability (LPSP) limit of 0.05 lowered the annualized total cost by about 21.2% relative to the baseline without that constraint.
What scale and assets did the El-Kharga case represent?
The case models an islanded microgrid at El-Kharga Oasis supplying 240 apartments in 12 buildings and assumes 20 EVs and 10 Toyota Mirai hydrogen vehicles within the community.
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