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# How to enforce non-anticipativity in microgrid sizing optimization ?

Pierre Haessig, Jean Fructhieu Nikiema, Nabil Sadou, Elsy El Sayegh

► To cite this version:

Pierre Haessig, Jean Fructhieu Nikiema, Nabil Sadou, Elsy El Sayegh. How to enforce non-anticipativity in microgrid sizing optimization?. Openmod Grenoble workshop 2024, Mar 2024, Grenoble, France. . hal-04543567

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Submitted on 12 Apr 2024

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Distributed under a Creative Commons Attribution 4.0 International License
# How to enforce non-anticipativity in microgrid sizing optimization ?

Pierre Haessig, Jean Fructhieu Nikiema, Nabil Sadou, Elsy El Sayegh

► To cite this version:

Pierre Haessig, Jean Fructhieu Nikiema, Nabil Sadou, Elsy El Sayegh. How to enforce non-anticipativity in microgrid sizing optimization?. Openmod Grenoble workshop 2024, Mar 2024, Grenoble, France. . hal-04543567

HAL Id: hal-04543567

https://hal.science/hal-04543567v1

Submitted on 12 Apr 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>
# How to enforce ♦ non-anticipativity ♦
in microgrid sizing optimization ?

Pierre Haessig¹, Jean Fructhieu Nikiema¹, Nabil Sadou¹, Elsy El Sayegh²
¹ CentraleSupélec, IETR | ² EDF R&D Saclay

## Microgrid optimal design: definition & challenges

### Microgrids

Definition: **standalone energy systems**,
e.g. needed for rural electrification

**Microgrid design objectives:**

(min) Lifecycle cost = Investment + Operation + Replace
(max) Quality of Service (→ min load shedding)

and also: environmental impact...

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

Two intertwined optimization problems:

* **Microgrid sizing**: find the optimal components' size
* **Energy management (EM)**: choose, at each instant, the optimal power flows

Fact: due to uncertainty of project data, **anticipative** Energy Management may not be applicable on field.

Q: Which **microgrid model formulation** yield:

* reliable & **fast** convergence? (~ in seconds)

* **non anticipative** operation?

### Microgrid sizing tool

Since each project is a specific design
→ need for reliable microgrid sizing tool

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

## Different formulations for sizing & operation optimization

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

## Blackbox sizing with embedded energy management

1 & 2

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

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

### Tools

<figure><img src="image_8.png" /></figure>

Microgrids.X

<figure><img src="image_9.png" /></figure>

https://pierreh.eu/Microgrids-X/
Github: Microgrids-X/Microgrids.jl

Operational & economic simulation of Microgrids

- with simple rule-based energy management

- with sizing optimization example

<table><thead><tr><th>Three implementations</th><th>NEW</th></tr></thead><tbody><tr><td><strong>Julia</strong><br>the fastest one:<br>0.15 ms/sim</td><td>Algebraic variant:<br><strong>JuMP</strong> based<br>in progress...</td></tr></tbody></table>

## Conclusions

### No clear winning approach

**Non anticipative** energy management typically implies **blackbox** sizing optimization with either:

* simplistic energy system (rule-based control)

or

* prohibitively slow evaluation time (optimization-based energy management)

Algebraic sizing optimization models typically assumes **anticipative** ("perfect foresight") energy management

### Perspectives

For blackbox optimization:

- Use Bayesian optimization to circumvent the increased simulation time due to optimization-based energy management (Model Predictive control, Stochastic Dynamic Programming)

For algebraic optimization:

- Is there an efficient way to enforce **non anticipative** Energy Management?

<figure><img src="image_10.png" /></figure>
# How to enforce ♦ non-anticipativity ♦
in microgrid sizing optimization ?

Pierre Haessig¹, Jean Fructhieu Nikiema¹, Nabil Sadou¹, Elsy El Sayegh²
¹ CentraleSupélec, IETR | ² EDF R&D Saclay

## Microgrid optimal design: definition & challenges

### Microgrids

Definition: **standalone energy systems**,
e.g. needed for rural electrification

**Microgrid design objectives:**

(min) Lifecycle cost = Investment + Operation + Replace
(max) Quality of Service (→ min load shedding)

and also: environmental impact...

Two intertwined optimization problems:

* Microgrid **sizing**: find the optimal components' size
* Energy management (EM): choose, at each instant, the optimal power flows

Fact: due to uncertainty of project data, **anticipative** Energy Management may not be applicable on field.

Q: Which **microgrid model formulation** yield:

* reliable & **fast** convergence? (~ in seconds)

* **non anticipative operation**?

### Microgrid sizing tool

Since each project is a specific design
→ need for reliable microgrid sizing tool

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

## Different formulations for sizing & operation optimization

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

## Blackbox sizing with
embedded energy management

1 & 2

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

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

## Algebraic all-in-one sizing
& energy management

3 & 4

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

Issue: anticipative ("perfect foresight") energy management by default

### Tools

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

Microgrids.X

<figure><img src="image_8.png" /></figure>

https://pierreh.eu/Microgrids-X/
Github: Microgrids-X/Microgrids.jl

Operational & economic simulation of Microgrids

- with simple rule-based energy management

- with sizing optimization example

Three implementations

**Julia**
the fastest one:
0.15 ms/sim

**Matlab**
Python
with in-browser
demo version

**NEW**
Algebraic variant:
**JuMP** based
*in progress...*

## Conclusions

### No clear winning approach

**Non anticipative** energy management typically implies **blackbox** sizing optimization with either:

* simplistic energy system (rule-based control)

or

* prohibitively slow evaluation time (optimization-based energy management)

Algebraic sizing optimization models typically assumes **anticipative** ("perfect foresight") energy management

### Perspectives

For blackbox optimization:

- Use Bayesian optimization to circumvent the increased simulation time due to optimization-based energy management (Model Predictive control, Stochastic Dynamic Programming)

For algebraic optimization:

- Is there an efficient way to enforce **non anticipative** Energy Management?