Electricity customers usually have an uneven load profile during the day, resulting in load peaks. Load peeks, or Peek Demand, can be long term, lasting for hours, or bursts, lasting only for couple of seconds or minutes. They could be daily or could happen a couple of days per month, depending on the consumer load profile and operations requirement.
The power system has to be dimensioned for that peak load while during other parts of the day it is under-utilized. The extra costs in keeping up with the peak demand are passed to the customers in form of a power fee so the utility can secure spinning reserve. In many utilities these billing structure can substantially increase the consumer monthly billing expense. At the same time, the electrical supply has to be designed to deal with the highest peak demand. Even if the consumer average demand is far lower, due peak demand, a substantial capital expenditure in the electrical infrastructure will be required.
By utilizing an Energy Storage System, peak load can be reduced and hence the power fee. The BESS is controlled to charge up during off-peak hours or when renewable energy is available and discharged during peak hours. Consumer peak loads often coincide with the peak load of the overall grid. That means the cost of energy is also high during these times.
In such cases the benefit of peak shaving is double; by reducing both the power fee and the cost of energy. Peak shaving can also be used by utilities or plants of renewable energy to increase the capacity of the existing grid infrastructure. Transmission and Distribution upgrades can be deferred into the future providing a more cost-efficient upgrade path for the power system.
The amount of peak power that can be reduced by an BESS is limited by its energy storage capacity, its maximum charge and discharge powers, and the load characteristics, meaning how much energy the load peaks hold. ProWatts Energy proposed method aims to find the optimal peak shave level by utilizing optimization algorithms to find the optimal shave level based on recorded historical data. By applying statistical analysis and optimal shave level, with the confidence interval of completely utilizing, the available energy can be provided.