Optimal Sizing and Techno-Economic Feasibility of Hybrid Microgrid
Abstract
:1. Introduction
- A design that combines PV–battery–hydrogen with grid connection to meet both short-term (battery) and long-term (hydrogen) storage needs.
- Dual-objective optimization that combines the minimization of the levelized cost of electricity (LCOE) with the restriction of the loss of power supply potential (LPSP) to balance cost and reliability.
- Bidirectional grid modeling to explicitly consider import costs (α) and export revenues (β).
- A replicable model for MENA universities leveraging Egypt solar potential and green hydrogen ambitions.
2. Literature Review
3. MG System Description
3.1. Operation Modes
3.2. Load Profile and Power Demand Assessment
3.3. Load Demand Profile Modeling
3.4. Simulation Tools and Data Sources
- ➢
- Software: MATLAB R2023a (Optimization Toolbox) for component sizing; HOMER Pro 3.14 for sensitivity analysis.
- ➢
- Weather Data: Solar irradiance/temperature from NASA POWER (26.3° N, 31.4° E) at 1 h resolution.
- ➢
- Load Profiles: Measured campus demand (2023–2024) with ±5% uncertainty, as shown in Figure 2.
- ➢
- Cost Data: As shown in Table 4.
4. Proposed Sizing Method
4.1. PV Generator Cost
- CPV = Total cost of the PV system;
- PPV = Power produced by the PV system (in kW);
- CMPV = Maintenance cost per unit power (USD/kW);
- FPV = Fixed costs (e.g., PV panel purchase price, installation, permits, inverters, and other non-scalable costs).
4.2. Battery Energy Storage Cost
- Cb = Total cost of the BES;
- Pb = Power capacity of the BES (in kW);
- CMb = Maintenance cost per unit power (USD/kW);
- Fb = Fixed costs (battery purchase price, installation, control systems, etc.).
4.3. Hydrogen Generator Cost
- CHG = Total cost of the HG;
- PHG = Power output of the HG (in kW);
- CMHG = Maintenance cost per unit power (USD/kW);
- NHG = Fixed costs (HG purchase price, installation, equipment, inverters, etc.).
4.4. Grid Interaction Costs
- α = The price per kWh of electricity purchased from the grid;
- Pgrid > 0 = MG imports power from the grid at rate α (USD/kWh).
- β = The price power selling rate to the grid in USD/kWh.
- Pgrid < 0 = MG exports surplus power to the grid at rate β (USD/kWh).
4.5. Transition Sequence Between Modes
- Daytime (PV Active):
- ▪
- Priority 1: Serve load directly from PV.
- ▪
- Priority 2: Charge battery to SOC = 90%.
- ▪
- Priority 3: Export to the grid if β > 0.021 USD/kWh.
- Sunset Transition (PV Ramp-Down):
- ▪
- Trigger: dP_PV–dt < −10% P_rated per hour.
- ▪
- Action:
- -
- Phase out exports over 15 min.
- -
- Initiate battery discharge at a 50% rate.
- Nighttime (PV Inactive):
- ▪
- Primary source: Battery until SOC = 40%.
- ▪
- Secondary: Fuel cell if P_load > 1.2 × P_battery_rated.
- ▪
- Tertiary: Grid import if α < 0.027 USD/kWh.
- Grid Interaction Protocol
5. Levelized Cost of Electricity
5.1. Total Annualized Cost
5.2. Sizing Algorithm
- CNPC denotes the minimum net present cost, which includes the costs of the PV system, batteries, the inverter, and the converter;
- CAPEX denotes capital expenditures;
- OP&MEX denotes the operating and maintenance expenditures.
5.3. Loss of Power Supply Probability Calculation
- The program first asks for the properties of the system parts and determines the minimum and maximum configurations for the system while adhering to all previously set restrictions.
- Power generated by the various components is determined, which changes every hour depending on the weather, starting with the minimal configuration so achieved.
- The LPSP value given in (14) is computed.
- The configuration is saved, its cost is determined, and a new configuration is examined if the LPSP acquired is negative or equal to zero. The current configuration will not be preserved if this value is positive, and a fresh one will be examined instead.
- The less expensive configuration will be the one that solves the optimization problem within the set of possible configurations.
5.4. Discounted Cash Flow Analysis
6. Results and Discussion
6.1. Simulation Results
6.2. Sizing Algorithm Results for 100 kW Microgrid
- Optimal Sizing Methodology
- B.
- Sensitivity Analysis
- -
- PV systems: Annual output reduction of 0.8% (typical for monocrystalline modules).
- -
- Battery systems: Capacity fades to 80% of initial capacity after 5000 full cycles.
- -
- Electrolyzer membranes: 15% efficiency loss over 60,000 operating hours.
- -
- PV soiling losses: 12% reduction in output (monthly cleaning assumed).
- -
- Temperature effects:
- PV derating: −0.5%/°C above STC.
- Battery efficiency penalty: 5% reduction at 35 °C ambient.
7. Conclusions
- Integrated hybrid energy system with dual storage:
- ○
- Unlike conventional PV–battery systems, this study introduces a PV–battery–hydrogen hybrid MG, combining short-term (battery) and long-term (hydrogen) energy storage to address intermittency and ensure reliability.
- ○
- The hydrogen subsystem (electrolyzer, storage tank, and fuel cell) provides seasonal storage capability, a critical advantage over battery-only systems in regions with fluctuating solar availability.
- Dual-objective optimization balancing cost and reliability:
- ○
- The levelized cost of electricity (LCOE) and loss of power supply probability (LPSP) are optimized simultaneously, ensuring cost-effectiveness (LCOE: 0.005–0.015 USD/kWh) while maintaining high reliability (LPSP ≤ 5%).
- ○
- This approach outperforms single-objective optimization methods found in prior studies, providing a balanced trade-off between economic and technical performance.
- Bidirectional grid interaction with realistic pricing:
- ○
- Unlike simplified grid models, this work explicitly accounts for import costs (α) and export revenues (β), reflecting Egypt’s real-world electricity tariffs.
- ○
- The economic model demonstrates how energy trading with the grid enhances financial viability, reducing payback periods to 8–10 years.
- Replicable model for MENA universities:
- ○
- This study provides a scalable methodology for universities in the Middle East and North Africa (MENA), leveraging high solar potential (5–7 kWh/m2/day) and Egypt’s emerging green hydrogen policies.
- ○
- The operating modes and control strategies are generalizable, making the system adaptable to other academic or institutional microgrids.
- Techno-economic validation via advanced simulation tools:
- ○
- The combined use of MATLAB (for optimization) and HOMER Pro (for sensitivity analysis) ensures robust sizing and performance validation, addressing uncertainties in load demand and solar variability.
- ○
- The economic analysis confirms 20–30% cost savings over 20 years compared to conventional grid reliance, alongside 90–100 tons/year CO2 reduction.
8. Future Research Directions
- ➢
- Probabilistic sizing methods to account for dust storms, demand growth, and component degradation.
- ➢
- Integration with electric vehicle (EV) charging stations to enhance campus sustainability.
- ➢
- Policy incentives analysis to accelerate adoption across MENA universities.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
AC | Alternating Current | HSA | Harmony Search Algorithm |
ACS | Annualized Cost System | IEC | International Electro-Technical Commission |
BCR | Benefit–Cost Ratio | IRR | Internal Rate of Return |
BES | Battery Energy Storage | KSA | Kingdom of Saudi Arabia |
CO2 | Carbon Dioxide | KVA | Kilo Volt Amperes |
DC | Direct Current | LCOE | Levelized Cost of Electricity |
DCFA | Discounted Cash Flow Analysis | Li-Ion | Lithium-Ion |
DER | Distributed Energy Resource | LPSP | Loss of Power Supply Potential |
DG | Diesel Generator | MENA | Middle East and North Africa |
DGs | Distributed Generators | MG | Micro-Grid |
EMS | Energy Management System | MPC | Model Predictive Control |
ESS | Energy Storage System | MPPT | Maximum Power Point Tracking |
EV | Electric Vehicle | NPC | Net Present Cost |
FA | Firefly Algorithm | PEME | Proton Exchange Membrane Electrolyzed |
FC | Fuel Cell | PSO | Particle Swarm Optimization |
GA | Genetic Algorithm | PV | Photovoltaic |
GW | Gigawatt | RES | Renewable Energy Source |
H2 | Hydrogen Storage | ROI | Return on Investment |
HESS | Hybrid Energy Storage System | SFLA | Shuffled Frog Leaping Algorithm |
HMGS | Hybrid Micro-Grid System | SOC | State of Charge |
HOMER | Hybrid Optimization of Multiple Energy Resources | WACC | Weighted Average Cost of Capital |
HPS | Hybrid Power System | WT | Wind Turbine |
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Mode | Trigger Condition | Primary Action | Critical Parameters |
---|---|---|---|
Normal Daytime (High Solar) | Solar irradiance > 600 W/m2 | PV powers load → excess charges battery → surplus to electrolyzer (H2 production) | PV output, battery SOC (60–90%), load demand |
Battery Charging | PV generation > load + battery SOC < 90% | Excess PV energy charges the battery | Battery charge rate, SOC limits |
Hydrogen Production | Battery SOC = 90% + PV surplus | Electrolyzer activates (1.8–2.5 kg H2/h at 60–80% efficiency) | Electrolyzer capacity, H2 storage pressure (30–80 bar) |
Nighttime (Battery Discharge) | Solar irradiance = 0 | Battery discharges → FC supplements if SOC < 40% | Battery SOC, FC hydrogen consumption (6 kg/h) |
Grid-Connected | Grid available + surplus/deficit | Export excess to the grid (β = 0.021/kWh) or import (α = 0.027/kWh) | Grid tariffs (α, β), import/export limits |
Islanded (Off-Grid) | Grid failure | PV + battery + FC sustain load; load shedding if deficit | LPSP (<5%), H2 reserve, battery SOC |
Building | Area | Air Conditioning | Specific Load | Total kW per Floor | Total kW for 3 Floors Build. | KVA Assuming 0.7 PF |
---|---|---|---|---|---|---|
Dean building | 1897 | 100 W/m2 | 30 W/m2 | 246 | 738 | 1054 |
Lecture building | 2814 | 100 W/m2 | 30 W/m2 | 366 | 1098 | 1568 |
MG Components | Parameters | Symbols | Value/Unit |
---|---|---|---|
PV Array | Rated power | Prat | 305 W |
Voltage at Max. power | Vmp | 54.7 V | |
Current at Max. power | Imp | 5.58 A | |
Open circuit voltage | Voc | 64.9 V | |
Short circuit current | Isc | 5.98 A | |
Dimensions | 61.3 × 41.2 × 1.8 in (1557 × 1046 × 46 mm) | ||
Weight | ~18.6 kg | ||
Sun-Power SPR-305E-WHT-D modules with a 100 kW rating that are 330 (Nser = 5 Npar = 66) in series and parallel configuration | |||
BES | Li-ion battery | A 48 V, 500 Ah | |
Battery SOC | SOCmin–SOCmax | 60–90% | |
Electrolyzer | Capacity | 50–100 kW | |
Efficiency | 60–80% | ||
Hydrogen production rate | 1.8–2.5 kg/h | ||
Operating pressure | 30–80 bar | ||
Hydrogen Storage | Capacity | 50–70 kg | |
Pressure (compressed gas) | 350–700 bar | ||
Fuel Cel | Capacity | 100 kW | |
Efficiency | 40–60% | ||
Hydrogen consumption rate | 6 kg/h | ||
Inverter and Converter | Bidirectional DC-DC Converter | A 50 kW, controlled voltage/current outputs | |
Bidirectional hybrid inverter system | A 120 kVA, 400 V AC, 270 V DC input, 50 Hz | ||
AC Distribution System (400 V AC Bus) | Cable type | XLPE | (cross-linked polyethylene) |
Length | 1 km | (typical for campus-scale MGs) | |
Resistance | R | 0.05 Ω/km | |
Reactance | X | 0.04 Ω/km | |
Voltage tolerance | ±5% (380–420 V) | complies with IEC 60038 standards [63] | |
Power losses | 3.1 kW | (3.1% loss, acceptable) |
Component | Cost | Value | Unit | Reference Sources |
---|---|---|---|---|
PV System | Capital cost | 2000 | USD/kW | [66,67] |
O&M cost | 10 | USD/kW/y | ||
Lifetime | 25 | years | ||
A 50 kW PEM Electrolyzer | Capital cost | 1200 | USD/kW | [68,69] |
Lifetime | 60,000–90,000 | hours | ||
Hydrogen Storage (compressed gas) | Capital cost | 25,000–70,000 | USD (50–100 kg at 350 bar) H2 | [70] |
Lifetime | 15–20 | years | ||
Fuel Cell | Capital cost | 1000–3000 | USD/kW | [71] |
Lifetime | 30,000–40,000 | hours | ||
BES (Li-ion) | Capital cost | 400–600 | USD/kWh | [72] |
Replacement cost | 200 | USD/kWh | ||
O&M cost | 5 | USD/kWh/y | ||
Throughput | 3000 | kWh/unit | ||
Round-trip efficiency | 9 | % | ||
Inverter and Converter (bidirectional) | Capital cost | 200–500 | USD/kW | [73] |
Replacement cost | 200 | USD/kW | ||
Lifetime | 15 | years | ||
Efficiency | 95 | % |
Condition | Action | Control Parameters |
---|---|---|
P_excess > 10% P_rated AND β > LCOE | Export to grid | Ramp rate: 5%/min of P_rated |
P_deficit > 5% P_load AND α < H2_cost | Import from grid | Max import: 80% of grid connection capacity |
Component | Optimal Size | Sizing Criteria | Feasibility Proof |
---|---|---|---|
PV Array | 120 kWp | Meets 120% of average daily load (72 kW) | NASA irradiance data + 20% oversizing for haze |
Battery (Li-ion) | 150 kWh | Covers 6-h nighttime load (40 kW × 6 h) | SOC constraints (60–90%) + 5000-cycle lifespan |
Electrolyzer | 50 kW | Matches surplus PV (>80 kW for 4 h/day) | H2 production rate (1.8 kg/h) and 65% efficiency |
Fuel Cell | 30 kW | Supplies 75% of peak deficit (112 kW–80 kW) | 40% efficiency + 6 kg-H2/h consumption |
H2 Storage | 60 kg | Stores 3 days of FC demand (18 kg/day) | 350-bar compression + 98% storage efficiency |
Metrics | Details |
---|---|
System Cost | Capital Cost: 150,000 USD–250,000 USD LCOE: 0.005–0.015 USD/kWh. |
Emissions | CO2 reduction: 90–100 tons/year Near-zero carbon emissions are possible with a renewable-powered system |
Reliability | Energy autonomy due to complementary energy sources Grid independence during outages Enhanced power quality through battery and hydrogen storage |
Capacity Factor | 18–22% (for PV energy in Sohag, Egypt) |
Energy Resilience | Continuous power supply during peak demand or outages; extended operation with hydrogen storage |
Sustainability | Aligns with global decarbonization goals; promotes renewable energy usage |
Payback Period | 8–15 years |
Return on Investment | ROI: 6–10% over 25–30 year system lifespan |
Battery Storage | 100–200 kWh capacity provides 2–6 h of short-term storage |
Hydrogen Storage | Long-term storage with ~60–70% efficiency, ideal for seasonal or extended outages |
Environmental Impact | Building rooftops use: 800–1000 m2 for PV energy Water usage: 9 L/kg of hydrogen produced |
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Saleeb, H.; El-Rifaie, A.M.; Sayed, K.; Accouche, O.; Mohamed, S.A.; Kassem, R. Optimal Sizing and Techno-Economic Feasibility of Hybrid Microgrid. Processes 2025, 13, 1209. https://doi.org/10.3390/pr13041209
Saleeb H, El-Rifaie AM, Sayed K, Accouche O, Mohamed SA, Kassem R. Optimal Sizing and Techno-Economic Feasibility of Hybrid Microgrid. Processes. 2025; 13(4):1209. https://doi.org/10.3390/pr13041209
Chicago/Turabian StyleSaleeb, Hedra, Ali M. El-Rifaie, Khairy Sayed, Oussama Accouche, Shazly A. Mohamed, and Rasha Kassem. 2025. "Optimal Sizing and Techno-Economic Feasibility of Hybrid Microgrid" Processes 13, no. 4: 1209. https://doi.org/10.3390/pr13041209
APA StyleSaleeb, H., El-Rifaie, A. M., Sayed, K., Accouche, O., Mohamed, S. A., & Kassem, R. (2025). Optimal Sizing and Techno-Economic Feasibility of Hybrid Microgrid. Processes, 13(4), 1209. https://doi.org/10.3390/pr13041209