To efficiently utilize energy drawn on renewable energy sources in micro-grids, this project explores the flair of machine learning in smart energy control systems, by implementing a custom advanced AI algorithm for cheaper Sustainable Energy Management In Micro-grids

1. Fabian Cheruiyot
Software Engineering Apprentice.
ALX - Holberton School
Graduate Electrical and Electronics
Engineer. University of Nairobi
AI aficionado
Ann Mulwa
Electrical and Electronics
Engineering Student.
University of Nairobi
AI enthusiast
TEAM: METAMINDS

2. AI for GREEN
An advanced AI algorithm for cheaper Sustainable Energy
Management In Microgrids

3. North Star: The worlds Twin Energy Problems
87% Carbon Emissions is as a
direct result of Energy
production
Energy Poverty.
Focus on Sub-Saharan Africa

4. Microgrids : Green at the end of the tunnel?
Scaling up the implementation of Microgrids
centered on renewable energy sources
promise.
• Improved energy resilience
• Reduced carbon emissions
• Energy market Liberalization
• Reduced energy costs.
Microgrids offer a front towards curbing carbon emissions while bridging the energy gap
in sub-Saharan Africa.

5. Africa’s wind energy potential = 250 times
continents electricity demand.
Africa is home to 60% of the best
solar resources globally,
Africa generates 9% of its
total energy demand from
renewable
sources
600 million people lack access to
electricity in sub-Saharan Africa.
✔ 3,000 installed microgrids today. Further
160,000 are required to offset the
continents energy gap
✔ The cost of electricity produced by mini grids
could be as low as $0.02/kWh by 2030
✔ The World Bank has committed more than $1.4
billion to mini grids over the next seven years.
(World Bank Data 2023)

6. Capped Potential? Limitations in Microgrids Today
WHAT IF?
❑ We could cheaply and efficiently utilize
energy forecasts to Optimize energy
dispatch in microgrids?
❑ We could Dynamically buy or sell power
to the grid in a way that minimizes our
energy costs?
❑ We could autonomously plan our non-
critical loads using forecasted energy
data for minimum energy cost
implication?
Economic Viability:
• Establishing microgrids is expensive.
• Cost justification is thus dependent on the
energy cost savings from microgrids.
Curtailment of Renewable energy resources:
• Intermittent nature.
• Energy Imbalance
Integration with the Main Grid:
• Efficient integration without disruptions or
imbalances.
• The Duck Curve: Steep drop and rise in energy
demand due to solar power fluctuations.

7. Master Slave Artificial Humming Bird Algorithm for model Predictive
Control/Management of Energy in Micro-Grids
An Improved computational intelligence algorithm
for enhanced performance.
The Artificial Hummingbird Algorithm (AHA)
simulates the special flight skills and intelligent
foraging strategies of hummingbirds
in nature
Classified under Metaheuristics, which are optimization algorithms
inspired by natural processes.
• Used in AI for solving complex problems efficiently
Enhanced by introducing a master slave learning technique:
-Search population is split into masters and slaves based on quality solutions
-Slave population learns from the masters to improve performance

8. To Guage the algorithms performance, we
subjected it to 23 Test functions
Comparison with 11 other optimization
algorithms show superior or optimum
performance in 75% of the subjected test cases

9. Results: MATLAB Simulation – Energy Dispatch In Microgrid Based
on Data from Laguna Grande Smart Micro-Grid in Peru
From mathematical model simulation of the Grid
connected micro-grid with MS-AHA Energy control
over 24 hour forecast, results estimate a:
• 5-15% increment in energy cost saving without
dynamic power pricing.
(grid power prices remain constant over
the 24 hour forecast period)
• 20-35% increment in energy cost saving with
dynamic power pricing
(grid power prices varies according to
demand over the 24 hour forecast period)

10. Novelty: Why this AI (optimization algorithm) way?
Integrating the master-slave AHA takes advantages of the algorithms strengths to perform energy optimization
with:
• Higher Speeds
• Higher accuracy
• Minimal computational power requirements
Schneider electrics EcoStruxure Microgrid Advisor Utilizes machine learning for forecasting and planning.

11. Reduced cost in computation resources.
Improved optimization accuracy further enhances energy cost savings cutting the
ROI of renewable energy microgrids by up to 1.4years.
Increased Versatility: Due to the compact dynamic nature of the MS-AHA, It can be
deployed remotely or on-site no matter the size or complexity of the microgrid, and
with negligible cost impact on initial investment.
Cost Impact of Improved Energy Optimization in
Microgrids

12. More than Just Cutting Costs: World Impact of AI-Optimized Micro-Grids
Improving energy optimization in microgrids will
aid in:
• Closing the energy gap by reducing energy
costs.
• Easing integration of microgrids with the
Grid
• Integration of more renewable energy
sources and different energy storage types, to
support wider load profiles in microgrids.
• Finally, today, cheaper energy means green
energy. AI-optimization prioritize green
energy, addressing the global climate crisis at
its core.

13. AI MS-AHA ROI METAHEURISTIC MACHINE LEARNING
MICROGRDS DUCK CURVE POWER DISPATCH MASTER-
SLAVE SIMULATION
SMART GRID DYNAMIC PRICING OPTIMIZATION EFFICIENCY
LOAD BALANCUNG COST MINIMIZATION CARBON EMMISIONS FUZZY DATA
ANALYTICS
AVEVA GO GREEN SCHNEIDER METAMINDS
SOLAR WIND ENERGY BATTERIES LINEAR PROGRAMMING
NATURE INSPIRED ARTIFICIAL HUMMINGBIRD ALGORITHM
Conclusion
With the power of AI our now palms. Lets use
emerging AI techniques for good
Or as we say it
Lets use AI for GREEN

14. AI MS-AHA ROI METAHEURISTIC MACHINE LEARNING
MICROGRDS DUCK CURVE POWER DISPATCH MASTER-
SLAVE SIMULATION
SMART GRID DYNAMIC PRICING OPTIMIZATION EFFICIENCY
LOAD BALANCUNG COST MINIMIZATION CARBON EMMISIONS FUZZY DATA
ANALYTICS
AVEVA GO GREEN SCHNEIDER METAMINDS
SOLAR WIND ENERGY BATTERIES LINEAR PROGRAMMING
NATURE INSPIRED ARTIFICIAL HUMMINGBIRD ALGORITHM
Thank You
We would like to extend our gratitude to Schneider and all the organizers of the
Go Green competition.
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