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In recent years, there has been a significant increase in demand for EVs. However, there are numerous challenges associated with EV charging, such as multiple EV charging strategy optimization. Numerous factors affect optimal charging strategies, but we will focus on the following:

1) Power Demand 

When installing multiple EV chargers in an office building, one must carefully consider power demand in order to avoid exceeding the maximum power demand resulting in hefty costs.

2) Variable Energy Price

Flat energy tariffs are becoming obsolete, and nowadays, time-of-use tariffs are on the rise resulting in price optimization.

The solution to multiple EV charging lies in mathematical optimization, which seeks to minimise the cost of electrical energy purchase while taking into account all constraints. We need to know the prediction of the building’s electrical energy consumption, maximum power demand, electrical energy price profile, and EV information. A flexible charging strategy for new arrivals should also be considered.

We will look at three different scenarios of multiple EVs charging under simplified conditions. At 8 a.m., eight EVs will arrive, each with the following characteristics:

30% initial battery state
scheduled departure time at 4 p.m
requested battery state at the time of expected departure 90%
battery capacity 80 kWh
maximum charging power 50 kW

Simulations

Using Energy Twin, the building’s power load predictions are calculated. In all three experiments you will see the energy profile of the office building, individual arrival of the EVs and the electrical energy price prediction for the day. 

The first scenario shows the basic strategy – EVs are charged immediately after being plugged in. The possible drawbacks of this strategy lie the high cost of energy in the morning, which correlates with the EVs‘ arrival time and high power demand.

In our previous post, we discussed how to manage an extensive building portfolio using Energy Twin and machine learning. This time we will show you how it can be further applied to another real-world example.

Energy Twin models can be used to forecast the future energy consumption profile, from a few hours to a few days ahead. This prediction was used as part of the control system for a virtual power plant’s Combined Heat and Power (CHP).

The virtual power plant’s goal is to use CHP and sell electrical energy when it is beneficial. In this case, heat is generated by both gas boilers and CHPs; however, CHPs have a lower gas-to-heat efficiency than gas boilers. As a result, the operator must prioritise gas boilers even if it contradicts the virtual power plant’s trading plan. Exceeding the contracted daily maximum gas consumption is far more expensive than the discrepancy in electrical energy delivery.

To avoid costly penalties, the model predicts daily gas consumption and is updated every hour. If a risk of exceeding occurs, the operator receives a notification with detailed information and suggested actions so that local technicians can respond in advance, if needed.