The project is funded by grants from the European Regional Development Fund.
The total value of the project and the amount co-financed by the EU (in HRK)
Automated planning of Smart Grid network includes highly accurate construction cost calculations.
Achieves at least a 98% data accuracy in comparison to the actual network situation.
Fully automated addition of network elements and sensors to the network management system within 24 hours.
Introducing fully digitized processes while achieving high levels of end-to-end performance.
Delivered as cloud services with complete data protection at all levels. Implementation period typically cut from 10 -12 months to several weeks.
Hardware infrastructure continually adapts to demands for resources from the system. The result is to reduce operating costs compared to existing Smart Grid management solutions.
– Increased focus on a customer engagement strategy to retain and attract customers who decide to produce their own energy through microgrids.
– The generation of renewable energy will now often be connected at the distribution level. This brings new opportunities for DNO’s to expand their market by selling energy directly to the consumer.
A Smart Grid is an electricity network that can cost efficiently integrate the behaviour and actions of all users connected to it – generators, consumers and those that do both – to ensure an economically efficient, sustainable power system with low losses and high levels of quality and security of supply and safety.
It employs innovative products and services together with intelligent monitoring, control, communication, and self-healing technologies to:
Optimize and predict electricity production.
By precise forecasting and better control of the production process, generators can place more profitable offers on the electricity sub-markets thus gaining additional profit.
Insights into electricity generation data.
Precise data will be available on the production of electricity and generators will gain valuable insights during operational tasks.
Optimize the planning, construction and upgrade of the network.
Determine reasons for a shortfall or surplus of electricity in the system and therefore more precisely and efficiently balance the electricity network for production and consumption.
Identify the location of energy losses through the adoption of new sensor technology.
Optimize the construction and upgrading of the network.
Monitor sales and purchase transactions within the new distribution network.
Identify the location of energy losses through the adoption of new sensor technology.
More accurately anticipate consumers’ daily consumption through improved forecasting.
Reduce deviation between forecasted and real energy consumption.
Reduce costs of buying energy on the electricity wholesale market.
Software to support installation and maintenance of smart meters.
Tools for retrieving, processing, and storing data from smart meters.
Support to reduce the consumption of energy by its customers.
Software to support proactive business behaviour.
Software to help reduce the energy consumption of customers.
Support coordination of electricity generation by clients.
Software to support installation, sale, and compensation payments of new renewable energy sources for small generators.
Get detailed insights into data on electricity consumption.
Optimize consumption through automatic electricity price notifications.
Physical laboratory implementation of the power production system.
SQL Server (opeross-server) and SQL database (UEDManagementDB).
Intermediate component responsible for communication between the power production facility and IoT Hub.
Access point for IoT data.
Data Processing Component.
Component for displaying real-time data.
Data view and laboratory control component.
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