AI Assisted Microgrid E-Mobility Operations

Speaker: Claude Philippe Medard, Principal Data Science, SAP Deutschland SE

Authors: Claude Philippe Medard, Nidhi Sawhney (Principal Data Scientist, SAP) and Jean-Christophe Pazzaglia (SAP LABS France AI CX Center Lead), SAP LABS France, Mougin France

How to develop smart investment strategies for E-Mobility solutions? The solution addresses challenges faced by microgrid asset owner for e-mobility operations where they need to regularly decide the best investment options in terms of energy options like grid, solar, battery and projected demand from smart buildings and electric vehicles. The constant challenge faced by microgrid asset operators are how to ensure running e-charging facilities at optimal cost, where to invest to ensure maximal self-consumption and minimizing waiting times, and how to plan based on different demand growth assumptions with right trade-offs between cost and service quality.

We investigate a solution that comprises of:
GenAI assisted Optimization Solution on SAP BTP
Harness the power of SAP BTP with Generative AI Hub combined with Mathematical Optimization to empower business users to simulate, optimize and action investment decisions.
Simulate multiple demand and supply scenarios to analyze impact on KPIs.

The solution (optim model) has multiple modes to cater to different decision horizons:
1. Operational demand & response plans for production of solar power and battery usage along with consumption of grid in view to fulfill constant and ready to wait energy demands.
Demand Predictions at different granularities together with with apis for solar power predictions.
Production and consumption plans are computed at the hourly level across years.

2. Extension with investment decisioning across 10 year horizons for various types of solar power production and battery storage capacities.

Execution wise: Integrate with SAP E-mobility, Cloud For Energy and Ariba to go from decision making to optimal decision execution.