Vatajankoski's full live network optimisation with Gradyent's Digital Twin 

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3 min read

Finnish new energy company Vatajankoski has partnered with Gradyent to optimise its heating system, reducing heat losses, energy costs, and CO2 emissions. The Digital Twin platform provides operators with a full understanding of the network behaviour in real time to allow issue identification and optimisation of supply temperatures and production portfolio operation. The company also uses Gradyent's user module to monitor user performance and simulate various scenarios to derisk CAPEX investments.  

Challenge 

Vatajankoski is a Finnish new energy company that delivers heat to around 12,000 customers across the cities of Kankaanpää and Niinisalo, supplying approximately 80 GWh of thermal energy annually. Vatajankoski has an ambitious sustainability agenda focused on achieving set CO2 emission reduction targets. 

To reduce heat losses, energy costs, and CO2 emissions all at once, Vatajankoski needed a solution that would enable a real-time network overview and operational excellence far beyond what traditional software tools allow.

Solution 

Vatajankoski decided to partner with Gradyent and use its Digital Twin platform to launch a project focusing on minimising heat losses via supply temperature optimisation and efficient production portfolio operation with source dispatch optimisation.  

Based on new insights, Vatajankoski will leverage the expertise of Gradyent and the Digital Twin to perform more than ten design studies to analyse and derisk various CAPEX investments. 

Results 

Insights that enable real-time network optimisation 

Gradyent's Digital Twin helps the company's operators to fully understand their network's behaviour in real time and thus quickly identify issues with differential pressures and temperatures at different locations in the network.  

In addition to delivering real-time insights, the Digital Twin optimises the network operation by providing the operators with a 24-hour schedule of supply temperatures and flow setpoints.  

The operating schedule is updated every 15 minutes to ensure that the optimisation accounts for changes to the forecasted heat demand, fuel cost, and electricity price - all the while ensuring that every customer receives their contracted temperatures, and all source limitations are respected.   

Gradyent.ai

Image: Vatajankoski

User performance monitoring and improvement 

Vatajankoski recognises that for comprehensive system optimisation, addressing user performance is essential. Using Gradyent's user module, the company can monitor user performance and then rank it according to system impact. This enables Vatajankoski to effectively prioritise which users to improve first. 

Quote

We chose Gradyent as our partner for multiple reasons. We believe that they have the expertise to utilise AI not only to optimise the heating network but also to efficiently integrate many different heat production sources and storages with each other at any given time. With the Digital Twin, we can also identify optimal locations for our future production plant and energy storage facilities. In addition, we found the Digital Twin user interface very user-friendly and easy to understand and use.

Gradyent.ai
Lauri Hölttä
Production Director at Vatajankoski

Efficient design studies and analyses  

The above features are all relevant to improve the current network. But what about simulating other scenarios, such as new user connections, network expansion, new heat sources, the addition of pumping stations, or any other change to the system?  

Vatajankoski leveraged the Digital Twin to do just that. Among the many scenarios investigated were a new pipeline serving additional users, adding electric boilers to their source portfolio, and exploiting waste heat from a planned data centre. By exploring various scenarios using the Digital Twin, Vatajankoski gained valuable insights into how the proposed modifications would affect network behaviour, economics, and operation.  

Understanding the impact of network expansion 

One of the scenarios involved a 6-kilometre expansion of the network connecting new users located north of Kankaanpää. Gradyent helped Vatajankoski understand the system impact of the expansion, as well as correctly dimensioning the new 6-kilometre pipeline.  

One of the main concerns from Vatajankoski was understanding if the expansion would result in any bottlenecks. By simulating the pressures, flows, and temperatures in the whole network, the Digital Twin was able to show that the expansion would not result in any bottlenecks.  

Exploiting waste heat and introducing E-boilers 

Additionally, Vatajankoski was looking to study the economical and operational effects of exploiting waste heat from a data centre and introducing E-boilers. Gradyent's Digital Twin was able to quickly analyse the economic impact of different dispatch strategies, showing that the new sources could reduce operating costs by 43%.  

The key to unlocking these savings is to only operate the electric boilers when the electricity prices are low and favouring production from the existing CHP (Combined Heat and Power) when electricity prices are high.  

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