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Energy Twin – Simulating Multiple Electric Vehicles Charging

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

 

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.

The second scenario takes variable energy prices into account, allowing the mathematical optimization to find the solution with the lowest charging price. That’s an improvement. The charging time, on the other hand, overlaps with the cooling demand of the building’s HVAC system, resulting in an even higher power demand. This disadvantage is addressed in the third scenario.

Power demand is added as a new constraint to the optimization problem in the third scenario. This means that the total of predicted own building consumption and EV charging power must be kept below the maximum power demand threshold. As a result, EVs are charged during the day at slightly higher rates in order to spread the charging and reduce the maximum power demand.

 

The above experiments were only hints at the difficulties associated with charging multiple EVs. The simulation demonstrates how critical it is to use machine learning and predictions to develop optimal charging strategies that allow us to better manage energy.

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Energy Twin – Using ML to prevent exceeding the contracted daily maximum gas consumption

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.

 
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Energy Twin – Example of utilising Machine Learning in the Energy Sector

Let us show you how Energy Twin utilises machine learning in a real-world example.

Take an extensive building portfolio. Human experts cannot oversee hundreds of key measurements in real-time. How can we efficiently monitor electrical energy consumption and detect anomalies?

The answer is – machine learning. Energy Twin analysed the electrical energy meter of more than 150 buildings. A comprehensive overview of anomalies across the entire portfolio can be provided within minutes with identified candidates for detailed inspection using models of prediction and measured data.

This way, experts spend their valuable time only on the problematic buildings and leave the tedious work of monitoring all of the buildings to Energy Twin.

Detected anomalies were caused by various reasons – from local heaters with no setback to air doors manual regime override or wrong setup of an electrical meter. Such faults can remain undetected without using machine learning, resulting in unnecessary building operation costs.

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Evaluation of Covid 19 Impacts on Energy Consumption Using Machine Learning

SkyFoundry has published a new Energy Twin case study. In this case study, we focus on the electrical energy consumption of three shopping malls during the covid 19 pandemics.

Using the Energy Twin, we can evaluate average weekly profiles for each period, compensating for the effect of the different outdoor temperatures. The Energy Twin model predicts how the energy consumption would look like with real weather conditions. We then compare the difference between measured data and Energy Twin model prediction.