In April, Atende Industries completed the implementation of smart metering – the redGrid system at Przedsiebiorstwo Energetyki Cieplnej in Konskie. The system was installed to calculate the capacity fee, which became mandatory from the beginning of 2021.
Przedsiębiorstwo Energetyki Cieplnej w Końskich Sp. z o.o. is a provider of distribution services and an electricity seller. The plant operates a 110/15/6 kV switching and transformer station. It is supplied double-sided through 110 kV lines. Two 16 MVA transformers are installed in the station.
As part of the cooperation, the entire metering infrastructure was replaced with smart meters ensuring remote communication. The redGrid system has been integrated with the customer’s measurement infrastructure and existing systems and is used for remote reading of measurement data from meters and calculation of the power fee. Thanks to this implementation, PEC in Końskie met the requirements imposed by the Energy Law, amended on April 15, seven years in advance.
The capacity fee is a cost resulting from the operation of the capacity market in Poland, which is valued at PLN 5.5 billion in 2021. The obligation to pay the capacity fee entered into force on January 1, 2021. The capacity fee is charged between 7:00 am and 10:00 pm : 00 on working days. The fee does not apply on public holidays and public holidays. The redGrid system, thanks to the possibility of remote reading and management of measurement data, is easily able to calculate energy bills, including power charges for any number of recipients. The system is scalable and can be implemented both at a large energy supplier (Energa-Operator) and smaller entities.
In addition to using the redGrid system, the client received test access to the besmart.energy cloud platform. It is used to collect data from smart meters, on the basis of which, in conjunction with the weather forecast model and measurement history, it predicts the consumption and production of energy from renewable sources. Within the model, it is also possible to simulate the behavior of energy storage in order to design systems aiming at full self-balancing. The system uses high-resolution weather models, machine learning methods and artificial intelligence solutions. On this basis, the system advises the user or may take action to balance the energy community (purchase / sale / storage / generation).