Client Case Study: ENEDIS
Project Overview: ENEDIS, a key player in the French energy distribution network, sought to optimize their power-to-grid solutions and accelerate the development of e-mobility infrastructure. With the increasing adoption of electric vehicles (EVs) and the rapid growth of renewable energy sources, ENEDIS faced the challenge of ensuring that their power grid infrastructure could support the future demand for clean energy while maintaining reliability and efficiency. To address this, they partnered with UpGradelle CS, leveraging AI, Model-Based Systems Engineering (MBSE), and Digital Twin technologies to create a robust, scalable solution.
Challenge:
ENEDIS needed to address several complex challenges to modernize their grid infrastructure and support the rapidly expanding electric vehicle network across France. These included:
- Optimizing energy distribution between renewable sources, electric vehicles, and the existing grid.
- Predicting and managing energy loads to prevent grid congestion and ensure a balanced power distribution.
- Integrating electric mobility (e-mobility) infrastructure, including charging stations, while ensuring grid stability.
- Increasing the scalability of infrastructure to handle future growth in both renewable energy production and electric vehicle adoption.
To meet these challenges, ENEDIS required advanced tools and strategies for real-time monitoring, predictive analytics, and simulation capabilities for grid management and e-mobility infrastructure.
Solution:
UpGradelle CS brought together a range of cutting-edge technologies and tools to deliver an advanced solution tailored to ENEDIS’s needs. The project combined AI, Digital Twin, and Model-Based Systems Engineering (MBSE) to optimize energy distribution and enable the efficient deployment of e-mobility infrastructure.
1. Digital Twin Technology for Real-Time Monitoring and Simulation:
UpGradelle’s team deployed Digital Twin technology to create virtual replicas of ENEDIS’s power-to-grid systems. This allowed for real-time data collection from various sensors and devices across the grid, including renewable energy generation sources, battery storage units, and electric vehicle charging stations.
- Real-Time Grid Management: The Digital Twin provided ENEDIS with a dynamic, real-time view of grid operations, allowing them to monitor energy flows, track usage patterns, and identify potential bottlenecks or inefficiencies.
- Simulations for Future Scenarios: By simulating different operational scenarios, the Digital Twin enabled ENEDIS to forecast future energy demands and grid stresses under various conditions, such as increased renewable energy generation or a higher volume of electric vehicles.
2. AI-Driven Predictive Analytics for Load Forecasting:
UpGradelle’s integration of Artificial Intelligence (AI) enabled ENEDIS to implement predictive analytics for power-to-grid optimization. AI models were trained on historical data, sensor inputs, and market conditions to predict energy demand patterns, optimizing the distribution of energy across different sectors.
- Predictive Load Management: The AI models provided ENEDIS with advanced forecasting of energy demand, allowing the company to proactively manage the flow of energy across the grid and ensure grid stability, particularly during peak demand periods.
- Demand-Response Optimization: AI-driven algorithms optimized the interaction between renewable energy sources, grid storage, and electric vehicles, adjusting energy flows based on real-time consumption patterns and availability of renewable energy.
3. Model-Based Systems Engineering (MBSE) for E-Mobility Infrastructure:
The project also involved the application of Model-Based Systems Engineering (MBSE) to design and manage the integration of e-mobility infrastructure into ENEDIS’s power grid. MBSE allowed for a structured approach to developing and testing the deployment of EV charging stations, ensuring that the infrastructure could support growing EV adoption without compromising the grid’s performance.
- System Modeling: MBSE enabled ENEDIS to model and simulate the entire e-mobility ecosystem, including charging stations, electric vehicles, and grid connections. This helped identify optimal locations for charging stations, ensuring they were positioned to minimize grid congestion and optimize energy distribution.
- Lifecycle Management: Through MBSE, ENEDIS was able to develop a clear roadmap for deploying and scaling their e-mobility infrastructure, integrating considerations for maintenance, scalability, and future technology upgrades.
Key Achievements:
- Enhanced Grid Stability: With the combination of Digital Twin and AI, ENEDIS was able to predict and manage energy demand fluctuations, preventing grid congestion and ensuring a steady flow of energy, even during peak periods of e-mobility use.
- Optimized Energy Distribution: By using AI-based predictive analytics, ENEDIS improved the efficiency of energy distribution between renewable sources, storage systems, and electric vehicles, reducing energy wastage and maximizing the use of clean energy.
- Faster and Scalable E-Mobility Deployment: MBSE tools enabled ENEDIS to rapidly deploy and scale e-mobility infrastructure across France, supporting the increasing adoption of electric vehicles while maintaining grid integrity.
- Cost Savings: The predictive maintenance and load management strategies developed using AI and Digital Twin technology helped ENEDIS reduce operational costs by preventing over-investment in infrastructure and minimizing energy wastage.
Quantitative KPIs and Performance Metrics:
The successful implementation of these technologies led to measurable improvements in ENEDIS’s operations, highlighted by several key performance indicators (KPIs):
- Grid Stability and Reliability:
- KPI: Percentage of time grid stability is maintained during peak energy consumption.
- Impact: The integration of AI and Digital Twin technologies resulted in a 15% improvement in grid stability, with fewer instances of overload or congestion during high-demand periods.
- Energy Distribution Efficiency:
- KPI: Percentage of renewable energy used for power-to-grid distribution.
- Impact: ENEDIS increased the share of renewable energy distributed across the grid by 20%, reducing reliance on non-renewable energy sources.
- Electric Vehicle Charging Utilization:
- KPI: Utilization rate of EV charging stations.
- Impact: The optimized placement and integration of EV charging infrastructure led to a 30% increase in station usage across key locations.
- Operational Cost Savings:
- KPI: Reduction in operational costs associated with energy grid management and infrastructure maintenance.
- Impact: The implementation of predictive maintenance and AI-driven optimization resulted in 10% annual cost savings in grid management and infrastructure maintenance.
- Time to Scale E-Mobility Infrastructure:
- KPI: Time taken to deploy a new charging station.
- Impact: The use of MBSE reduced the time required to deploy new EV charging stations by 25%, allowing ENEDIS to quickly scale their infrastructure in response to growing demand.
Conclusion:
The collaboration between ENEDIS and UpGradelle CS has set a new benchmark for optimizing power-to-grid solutions and accelerating e-mobility infrastructure development in France. By integrating AI, Digital Twin, and Model-Based Systems Engineering (MBSE), ENEDIS is now equipped with a cutting-edge, data-driven system that ensures grid stability, optimizes energy distribution, and supports the rapid scaling of electric vehicle infrastructure.
Through this innovative approach, ENEDIS not only enhances the sustainability of their energy grid but also accelerates the transition to a cleaner, greener, and more efficient energy system that is ready to support the future of mobility in France.