As electric vehicles (EVs) become more common, managing their charging demand presents new challenges – especially in buildings with limited grid connections. In this week’s newsletter we will explore how mathematical optimization can address these challenges, reducing costs and avoiding peak loads.
The Challenge: Shared Grid Connections
When multiple EVs share a grid connection with an office or residential building, uncoordinated charging can cause demand spikes, driving up energy costs and straining infrastructure. Traditional strategies – like immediate charging upon arrival – fail to consider electricity price fluctuations or building energy usage.
The Solution: Predictive, Demand-Aware Optimization
In the following scenarios, we demonstrate how combining machine learning with electricity price prediction and building load forecasting enables smarter EV charging strategies. The scenarios simulate a typical workday where eight EVs arrive at 8:00 AM with 30% battery charge and are required to depart by 4:00 PM with 90% charge.
Each of the following charts includes:
- An upper bar chart showing the predicted energy consumption of the building and the charging load of EVs 1-8
- A line chart representing the forecasted electricity price for the day
Scenario 1: Immediate Charging
In this scenario the charging starts immediately after plug-in.
This approach results in a peak demand of 854 kW and a total energy cost of €57. Because charging takes place during high-tariff periods, it leads to both elevated costs and significant power demand.
Scenario 2: Price-Based Optimization
Introducing optimization to minimise total charging cost.
Although the total electricity cost is reduced to €40, the peak demand rises to 960 kW as charging overlaps with the building’s HVAC cooling peak.
Scenario 3: Price &Power Optimization
In this final scenario, we introduce a constraint on maximum power demand, alongside the goal of minimizing total charging costs.
This approach results in a peak demand of 650 kW (the predefined limit), and a total electricity cost of €41.
When applied to a different day with varying electricity prices, the charging profile changes significantly – reflecting both price fluctuations and shifts in the building’s energy consumption.
These variations highlight a key insight: optimal charging strategies cannot be defined using static if-then rules. Instead, mathematical optimization is essential.
On some days, the most cost-effective strategy is to start charging immediately.
And if you time it well, you can even earn money.
While this is a simulation, it illustrates the complexity of real-world implementation. Achieving flexibility requires modular, multi-layered systems and open APIs, rather than rigid, single-vendor platforms. Ignoring energy flexibility can lead to increased costs, grid stress, and missed opportunities.
To move from simulation to real-world deployment, several challenges must be addressed:
- EV chargers’ interface
- Real-time access to building load data
- Online retrieval of electricity prices
- Data integrity and fail-safe mechanisms
- Fulfillment of all cybersecurity requirements
- Compliance with national grid codes and metering laws
- Risk of damaging battery health with poor control logic
- Responsibility for missed departures
As EV adoption grows, so does the need for intelligent charging strategies—especially in shared building infrastructures. Our simulations highlight that static rules aren’t enough; real-time optimizationbased on forecasts and constraints is essential.
While the path to real-world deployment involves technical and regulatory challenges, the benefits are clear: lower costs, reduced peak loads, and greater grid resilience. With the right tools and flexibility, EV charging can become a powerful part of smarter energy systems.
