5 ways in which data enables better production efficiency in a district heating plant

22 Apr 2020| Harri Lääveri
real-time data district heat

Data is at the core of improving the production efficiency of a district heating plant. The same goes for emission efficiency. Data is collected from the production plant, the district heating network, and the premises belonging to the network. SUPERSENSE compiles that data together, processes the data into a useful format, and enables the creation of clear visualisations.

Previously we have addressed the use of real-time and historical data to improve emissions and production efficiency in energy production at the surface level. Now we want to give concrete examples of ways in which data can be used to improve the production efficiency of a district heating plant.

 

1. A real-time snapshot of the state of the district heating network and production plant is the first step towards optimal production

Improving efficiency always starts with a full understanding of the current situation. Data is collected on the performance of the heating plant itself and, for example, on pressure differences in the district heating network. Most modern control room technologies are already gathering data on production. Data can also be easily collected from the district heating network.

It is therefore mainly a matter of compiling that data into one place, processing it into useful numerical indicators and presenting them in a clear format. By seeing in real-time how much should be produced at any given time, how much is actually being produced, and how much the network is capable of receiving, we are at a good starting point in terms of optimisation.

If desired, it is also possible to automate the optimisation.

 

2. Forecasting production needs based on historical data

Data that is monitored in real-time is simultaneously stored as historical data. When the data collected from production and the district heating network are enriched with, for example, weather data and other data that affected the conditions, accurate information about production and distribution can also be analysed. Based on this, SUPERSENSE is able to make accurate predictions about future production needs.

In this way, it is possible to optimise the operation of both the production plant and the district heating network in advance. This will, of course, lead to even better production and emission efficiency.

The data also provides more detailed information on maintenance needs. For example, changes in the pressure differences of heat pumps and their effect on production efficiency.

 

3. Optimal timing of maintenance outages

Power plants will inevitably have to suspend production from time to time during maintenance outages. Service interruptions are traditionally planned according to the age of the parts in use and are timed so that as many measures as possible can be taken at once.

The data enables better timing of maintenance outages, thus minimizing their impact on production efficiency.  For example, service interruptions can be scheduled according to when the need for production is at its lowest based on historical data. In addition, if there is more than one power plant, the production deficit due to a maintenance outage can be compensated for by other plants.

4. Avoiding overproduction leads to better emission efficiency

Although emission efficiency does not directly affect production efficiency, it has a significant impact on the profitability of a district heating company. By minimising emissions, an energy company may not use all of its emission permits (assuming, of course, that the company is covered by emissions trading) which can then be resold.

Naturally, efforts must also be made to reduce emissions for many other reasons.

 

5. Heating monitoring and optimisation services provided to properties

More and more district heating companies are offering heating monitoring and optimisation services for real estates. These are based on modern heat exchangers with IoT capabilities that send and receive data over the network. They collect information on the heating habits of the properties and reciprocally provide the property owner with heating optimisation as a service.

With this data collected from the heat consumption of properties, it is possible to further enrich the production demand forecasts and at the same time improve production efficiency even more.
 


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