Battery systems

FOR POWER BALANCE SERVICES

FOR POWER BALANCE SERVICES (TECHNICAL FLEXIBILITY)
Battery storage can provide services to stabilise the transmission system and provide technical flexibility:

Direct provision of CTPS services:
your storage can be connected directly to the CTPS system to help balance frequency or regulate voltage. You then compete online with other SVR providers and need to have the algorithms to be successfully activated when CEPS requests SVR service. This requires the know-how and
therefore it is easier to join the aggregation block called aggregators.

Through an aggregator:
if your installation is not large enough (e.g. 1 MW) or you do not want to compete online for activation (see previous paragraph), you can be part of a virtual power plant managed by an aggregator.
In order to participate in the system services, the storage must be compatible with the CEPS requirements and software integration with the flexibility management platform is required.

Key Issues:
What are the requirements for connection to the distribution network?
What is the warranty for the delivered solution?
What is the battery life in this mode of operation?
What are the service and safety requirements?
What are the legislative obligations arising from the provision of power balance services?

Financial efficiency:
The payback period for this solution is up to 3 years
Currently the highest financial efficiency

If you want to address this issue, arrange a free 30-minute consultation

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Why decide on us

WE ARE AGILE

As a result, we are able to respond effectively to changes and adapt to new situations that arise in each project.

you can rely on us

We consider reliability to be the foundation of trust and, above all, reliability significantly reduces the stress that is also present in any project.

Guarantee

Because we believe in our products, services and solutions, we are ready to take responsibility for their quality, which is corner stone of our efforts.

AI and data

For optimal operation and utilization of power  resources supplied by us, we employ machine learning methods in applications of predictive maintenance, management of solar hybrid and battery systems or estimation of future power generation.

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