Depots for EV fleets typically require considerable power levels (often in the MW range). These power requirements will have a significant impact on the business case for the fleet, both in terms of OPEX and CAPEX. The OPEX part is driven by demand charges. These charges are part of the electricity bill for a commercial property and can be more than 50% of the overall bill. They can be reduced significantly using load management techniques such as Smart Charging but for large depots this needs to leverage AI or similar optimization techniques to be successful. In this article we will take a look at these charges, consider how they affect the OPEX for a typical eMobility fleet and discover how they can be reduced using AI driven Smart Charging.
Demand Charges – What are they?
Demand charges are used by grid operators (DSOs, DNOs etc) to incentivize how the grid is used. Grid infrastructure is expensive to upgrade and typically has long lead times. The same is the case with generation assets. This means that the grid operator does not want to have peak power demands on their grid as they will need to provide the infrastructure to service this peak which may only be required for a couple of hours. To make the most efficient usage of the infrastructure (and therefore the capital invest) the grid operator wants to spread the demand over the day. One way of doing this is to incentivize the way in which commercial and industrial customers use the grid. Demand charges are one way of achieving this and therefore most commercial customers have two charges on their monthly bill driven by their electricity usage:
- Electricity Charges: This is the energy charge for the electricity they use (kWh) multiplied by the electricity price ($/kWh).
- Demand Charges: This is the energy charge for the amount of electrical capacity needed. It’s the maximum amount of power (kW) used during the highest usage 15-minute interval, multiplied by the local utility’s demand charge ($/kW).
In most cities there is some form of time of use tariff for electricity – this means that electricity (both the electricity charges and the demand charges) is cheaper at certain times in the day. The grid operator is trying to incentivize their customers to avoid using power and energy during peak periods (normally between 1600 and 2200 in the evening when most people are coming home and using electricity at home). So for EV fleet operators which typically require large amounts of power and energy, it becomes very important that the depot only uses that energy and power when it is the cheapest.
Why are Demand Charges Important…?
To illustrate the importance of demand charges it is best to look in detail at what happens within a typical large depot. In this example, the depot hosts 150 buses each requiring around 160 kWh of electricity every day. Furthermore, each bus can charge with 80kW of power and each bus is allocated a charger. The buses return to the depot in three waves and this can be seen in Figure 1 below where the number of buses returning i each half hour time period is shown.
With uncontrolled charging, each vehicle starts charging as soon as it is plugged in and this produces the power profile for the depot which has two main peaks of 4.6 MW and 3.9 MW with a further smaller peak at around 9am. Demand charges are based on the maximum power the customer uses and usually also dependent upon the time of day. To reduce the demand charges, there are two levers: Firstly, the power peaks need to be reduced, and secondly the power needs to be shifted away (load shifting) from the primary demand region (i.e. shifted to after 10 pm). Taking the charges of Con Edison, an energy company based in New York, as an example — there are two charge structures (https://www.coned.com/_external/cerates/historical-PSC10.asp). One for the summer and one for the rest of the year. The charge structure for summer can be seen in the figure below, where the depot power profile is also shown (Figure 2). It can readily be seen that the power peaks produced by the uncontrolled charging have a severe impact on the costs with the peak at around 9 to 9.30 pm being particularly decisive. It triggers high charges with the primary demand charge period (8 am to 10 pm) and the secondary demand charge period (all hours). The demand charges total $161,740 per month.
How can they be reduced….?
To reduce demand charges it is necessary to control and optimize the charging in the depot. A first step is to connect the charging stations into a monitoring and control system. The most efficient manner to do this is to link the chargers to a cloud-based IoT-system which collects data (typically more than twenty parameters, e.g. energy used by the charger, voltage or temperature) and enables the chargers to be controlled. Then the power of AI can be used to manage the charging schedules for each vehicle so that when they are combined, they deliver the optimal combination which delivers the lowest possible electricity bill for the depot. With one vehicle, this would be simple. Electricity is expensive at some points during the day and cheaper at others (typically after 10pm). So simply charge the vehicle after 10pm. With 5 vehicles, a human may still be able to find a combination of charging times that works but very quickly this type of problem can’t be solved manually And this is where some clever mathematics and computing power come into play, the so-called AI driven smart charging. The AI algorithms will search the multitude of possible charging schedules for each charger and vehicle to find the solutions which deliver the lowest cost. The optimal individual charging schedules for each charging station are then downloaded from the cloud control system using the inbuilt IoT-capabilities of the charging stations. Some more details on how this is achieved can be found in an earlier article https://ingenuity.siemens.com/2021/04/emobility-depots-how-do-we-ensure-that-every-vehicle-in-the-depot-is-charged-at-the-right-time-with-the-optimal-power-at-the-lowest-cost/.
The optimal power profile for the same depot, as determined by the AI algorithms, can be seen in Figure 3. The AI techniques have calculated 150 individual charging schedules (one each for the 150 vehicles) which when combined shift the energy usage out of the expensive time period and at the same time reduce the overall power level. The overall peaks have been significantly reduced from 4.6 MW to around 2.5 MW. Furthermore, the power usage has been shifted away from the primary demand charge region. Together these two improvements can reduce the demand charges per month by around 50 percent compared to the uncontrolled charging processes. This is achieved without degrading the operations within the depot, the vehicles are charged and ready to be deployed when needed.
Demand charges can be significant for commercial customers and can be a key lever in determining the successful business case for an electric fleet. As such uncontrolled charging which can produce large power peaks is a significant risk for the business case. To manage this risk, it is essential to employ digital solutions to manage the charging, reducing the power peaks and moving them away from expensive time periods. Siemens has a range of digital products to support fleets and depots. If you want to know more about charging management for fleets and depots then please come and talk to us. The easiest way to do this is to visit our website www.siemens.com/evdepot-digital. Here you can contact us to ask for more info or to book a slot for a demo or to talk to our experts.