# Sizing optimization for Islanded Microgrid with long-term Hydrogen Storage: A comparison of 2 rules-based operation strategy

Jean Fructhieu Nikiema, Pierre Haessig, Nabil Sadou, Hervé Gueguen

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Jean Fructhieu Nikiema, Pierre Haessig, Nabil Sadou, Hervé Gueguen. Sizing optimization for Islanded Microgrid with long-term Hydrogen Storage: A comparison of 2 rules-based operation strategy. Openmod Grenoble workshop 2024, Mar 2024, Grenoble, France. . hal-04622780

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Submitted on 24 Jun 2024

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Distributed under a Creative Commons Attribution 4.0 International License
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Jean Fructhieu NIKIEMA, Pierre HAESSIG, Nabil SADOU, Hervé GUEGUEN

Centrale Supélec - IETR, France

## Microgrid & problem description

<figure><img src="image_3.png" /></figure>

Which size for each component?

Our islanded Microgrid (100 REN) is composed of:

* Renewable sources - Photovoltaic modules and wind turbines

* **Short-term storage:** Li-ion batteries

* **Seasonnal storage:** PEM electrolyser, compressed Hydrogen tank and PEM fuel cell

**Problem:** Optimal components sizingintewined with the choice of the energy management system

**Contribution:**

* **2 rules based** energy management strategies proposed

* Techno-economic comparison of these 2 strategies and their impact on the optimal sizing

## Optimization Setup

<figure><img src="image_4.png" /></figure>

* Black Box optimization

* Tool link Github: Microgrids-X/Microgrids.jl

* Gradient-free algorithm used: GN_CRS2_LM from Nlopt.jl

* Maximal evaluation number: 100 000

* Optimization variables: component sizes

* 2 criteria have been considered; one economic: Levelized Cost of Energy (LCOE); and one for the Quality of Service: the shedding rate.

* Objective function: $f(X) = LCOE(X) + 10^5 \cdot \max((T_{shed}(X) - T_{shed\_max}), 0)$

<figure><img src="image_5.png" /></figure>

**Strategy 1: battery priority**

Strategy 1 will use the battery before the hydrogen chain to store excess energy or power the load in the event of a lack of renewable generation. Strategy 2 do the opposite by using primarily the power-H2-power chain.

**Strategy 2: Hydrogen priority**

**Note:**

* Batteries round-trip efficiency is 95%

* Power-H2-Power efficiency has been modelled to be equal to 30%

* PEM fuel and PEM electrolyser have a minimal running power equal to 5% of their nominal capacity.

* Simulations are made on 1 year with a time step of 1 hour. The results are then extrapolated over 25 years.

<figure><img src="image_6.png" /></figure>

## Results

### Optimization stats

* Wind capacity is limited to 1800 kWc in our case of study

* Average simulation time (~5ms)

* Average optimization time (~24s)

* Average evaluation number (~21000)

<table><thead><tr><th>EMS</th><th>T<sub>shed</sub></th><th>C<sub>pv</sub><br>(kWp)</th><th>C<sub>wind</sub><br>(kW)</th><th>C<sub>batt</sub><br>(kWh)</th><th>C<sub>el</sub><br>(kW)</th><th>C<sub>fc</sub><br>(kW)</th><th>C<sub>h2tank</sub><br>(kg)</th><th>LCOE<br>($/kWh)</th></tr></thead><tbody><tr><td>1</td><td>0%</td><td>4 001</td><td>1 800</td><td>5 489</td><td>866</td><td>1 537</td><td>16 270</td><td>0,3592</td></tr><tr><td>2</td><td>0%</td><td>6 681</td><td>1 800</td><td>262</td><td>1 901</td><td>1 423</td><td>17 000</td><td>0,4136</td></tr><tr><td>1</td><td>0,01%</td><td>4 001</td><td>1 800</td><td>5 492</td><td>864</td><td>1 435</td><td>16 238</td><td>0,3559</td></tr><tr><td>2</td><td>0,01%</td><td>6 649</td><td>1 800</td><td>198</td><td>1 911</td><td>1 389</td><td>16 990</td><td>0,4121</td></tr><tr><td>1</td><td>0,10%</td><td>4 033</td><td>1 800</td><td>5 406</td><td>863</td><td>1 240</td><td>15 923</td><td>0,3485</td></tr><tr><td>2</td><td>0,10%</td><td>6 546</td><td>1 800</td><td>132</td><td>1 935</td><td>1 241</td><td>16 805</td><td>0,4057</td></tr></tbody></table>

<figure><img src="image_7.png" /></figure>

## Observations & Conclusion

As might be expected for the 2 strategies, the higher the permissible load shedding rate, the lower the LCOE.

More renewable capacity with the EMS 2 due to the low efficiency of the Power-H2-Power chain.

More intensive use of the electrolyser and the fuel cell with the strategy 2. Which imply :

* Higher maintenance costs

* More frequent replacements

Strategy 2, is more sensible to fuel cell and electrolyser price variation.

In case of event of a significant fall in the manufacturing costs of these components, the competitiveness of the 2 strategies should be reassessed.

## Perspectives

Try more advanced EMS strategies based on predictive control or stochastic dynamic programming.

Study effect of these strategies on the optimization tool. If needed :

* Try Gradient-based algorithms

* Reduce the simulation time by performing it on representative days or weeks?

* Multistart with local optimization algorithms?

### Sensibility analysis

A sensitivity analysis have also been conducted on the electrolyser and fuel cell investment prices.

* **Case 1:** Respectively 500$/kW and 1000$/kW for PEMel and PEMfc in accordance with « Fuel Cell and Hydrogen Joint Undertaking (FCH 2 JU) » goals for 2030.

* **Case 2:** Respectively 2200$/kW and 2500$/kW for PEMel and PEMfc in case of slower industrialization than expected, and high raw materials prices

* For the **base case** prices were taken equal to 1600$/kW for both

<figure><img src="image_8.png" /><figcaption>LCOE for each scenario</figcaption></figure>

<figure><img src="image_9.png" /></figure>
## Microgrid & problem description

<figure><img src="image_2.png" /></figure>

Which size for each component?

Our islanded Microgrid (100 REN) is composed of:

* Renewable sources - Photovoltaic modules and wind turbines

* **Short-term storage:** Li-ion batteries

* **Seasonnal storage:** PEM electrolyser, compressed Hydrogen tank and PEM fuel cell

**Problem:** Optimal components **sizing** intewined with the choice of the **energy management system**

**Contribution:**

* **2 rules based** energy management strategies proposed

* Techno-economic comparison of these 2 strategies and their impact on the optimal sizing

## Optimization Setup

<figure><img src="image_3.png" /></figure>

* Black Box optimization

* Tool link Github: Microgrids-X/Microgrids.jl

* Gradient-free algorithm used : GN_CRS2_LM from Nlopt.jl

* Maximal evaluation number : 100 000

* Optimization variables : component sizes

* 2 criteria have been considered ; one economic: Levelized Cost of Energy (LCOE) ; and one for the Quality of Service : the shedding rate.

* Objective function : $f(X) = LCOE(X) + 10^5 \cdot \max((T_{shed}(X) - T_{shed\_max}), 0)$

### Strategy 1: battery priority

Strategy 1 will use the battery before the hydrogen chain to store excess energy or power the load in the event of a lack of renewable generation. Strategy 2 do the opposite by using primarily the power-H2-power chain.

### Strategy 2: Hydrogen priority

<figure><img src="image_4.png" /></figure>

**Note:**

* Batteries round-trip efficiency is 95%

* Power-H2-Power efficiency has been modelled to be equal to 30%

* PEM fuel and PEM electrolyser have a minimal running power equal to 5% of their nominal capacity.

* Simulations are made on 1 year with a time step of 1 hour. The results are then extrapolated over 25 years.

<figure><img src="image_5.png" /></figure>

## Results

### Optimization stats

* Wind capacity is limited to 1800 kWc in our case of study

* Average simulation time (~5ms)

* Average optimization time (~24s)

* Average evaluation number (~21000)

<table><thead><tr><th>EMS</th><th>T<sub>shed</sub></th><th>C<sub>pv</sub><br>(kWp)</th><th>C<sub>wind</sub><br>(kW)</th><th>C<sub>batt</sub><br>(kWh)</th><th>C<sub>el</sub><br>(kW)</th><th>C<sub>fc</sub><br>(kW)</th><th>C<sub>h2tank</sub><br>(kg)</th><th>LCOE<br>($/kWh)</th></tr></thead><tbody><tr><td>1</td><td>0%</td><td>4 001</td><td>1 800</td><td>5 489</td><td>866</td><td>1 537</td><td>16 270</td><td>0,3592</td></tr><tr><td>2</td><td>0%</td><td>6 681</td><td>1 800</td><td>262</td><td>1 901</td><td>1 423</td><td>17 000</td><td>0,4136</td></tr><tr><td>1</td><td>0,01%</td><td>4 001</td><td>1 800</td><td>5 492</td><td>864</td><td>1 435</td><td>16 238</td><td>0,3559</td></tr><tr><td>2</td><td>0,01%</td><td>6 649</td><td>1 800</td><td>198</td><td>1 911</td><td>1 389</td><td>16 990</td><td>0,4121</td></tr><tr><td>1</td><td>0,10%</td><td>4 033</td><td>1 800</td><td>5 406</td><td>863</td><td>1 240</td><td>15 923</td><td>0,3485</td></tr><tr><td>2</td><td>0,10%</td><td>6 546</td><td>1 800</td><td>132</td><td>1 935</td><td>1 241</td><td>16 805</td><td>0,4057</td></tr></tbody></table>

<figure><img src="image_6.png" /></figure>

## Observations & Conclusion

As might be expected for the 2 strategies, the higher the permissible load shedding rate, the lower the LCOE.

More renewable capacity with the EMS 2 due to the low efficiency of the Power-H2-Power chain.

More intensive use of the electrolyser and the fuel cell with the strategy 2. Which imply :

* Higher maintenance costs

* More frequent replacements

Strategy 2, is more sensible to fuel cell and electrolyser price variation.

In case of event of a significant fall in the manufacturing costs of these components, the competitiveness of the 2 strategies should be reassessed.

## Perspectives

Try more advanced EMS strategies based on predictive control or stochastic dynamic programming.

Study effect of these strategies on the optimization tool. If needed :

* Try Gradient-based algorithms

* Reduce the simulation time by performing it on representative days or weeks?

* Multistart with local optimization algorithms?

### Sensibility analysis

A sensitivity analysis have also been conducted on the electrolyser and fuel cell investment prices.

* **Case 1:** Respectively 500$/kW and 1000$/kW for PEMel and PEMfc in accordance with « Fuel Cell and Hydrogen Joint Undertaking (FCH 2 JU) » goals for 2030.

* **Case 2:** Respectively 2200$/kW and 2500$/kW for PEMel and PEMfc in case of slower industrialization than expected, and high raw materials prices

* For the **base case** prices were taken equal to 1600$/kW for both

<figure><img src="image_7.png" /><figcaption>LCOE for each scenario</figcaption></figure>