Emission requirements and transparency in district heating production
District heating networks have faced fewer regulations than electricity networks. The market itself has managed to keep end-user costs at a reasonable level, so there has been no need for public regulation as such. Producers and suppliers, through their voluntary actions, have been able to maintain efficiency in infrastructure, operations and safety, meaning there has been no need for authorities to intervene.
However, requirements relating to emission efficiency also apply to the production and distribution of district heating. The goals set by the Paris Climate Agreement (2016) and accepted by Finland are tough, so strong efforts must be made to achieve these goals.
The biggest challenge in the industry right now is the lack of real-time data, which is holding back the efforts of many to reduce emissions.
More details on the requirements
The EU’s energy and climate policy targets for 2020 were to reduce greenhouse gas emissions by 20% compared to the levels in 1990. The Paris Climate Agreement (2016), approved by Finland, set a target of 80% emissions reductions by 2050. Antti Rinne’s government has also set a goal for Finland to be carbon-neutral by 2035.
Based on data from Statistics Finland, the 2020 target is achievable, but as requirements increase, so must the measures taken to meet them.
Additionally, it is entirely possible that the requirements will become even more stringent over the next ten years as we gain new knowledge on the impact of emissions on the climate.
How does real-time data help meet requirements?
Reducing emissions from existing district heating plants is both a fuel and production efficiency issue. As regulations take a stance on fuels, it must be possible to significantly increase the use of renewable energy sources. In the context of district heating production, this means the use of biomass, or potentially the use of peat in combination with wood chips.
In terms of production efficiency, real-time data enables significant improvements at all levels. As this same data accumulates as historical data, machine learning can also be used to predict future production needs, which will allow for even greater achievements in emission efficiency.
Different levels of district heating and the benefits of data
Power plants often already have real-time information available on the process level, which is monitored in the plant’s control rooms. At this level, improvements in efficiency are due to greater utilisation and processing of that data. Processing the information generated into best practices for different situations improves both production and emission efficiency.
Often, the problem is that real-time data on both production and emission efficiency is missing on the plant level. Controlling emissions without real-time data on the state of production is very difficult. Once that data has been visualised and utilised, it is possible to optimise the plant’s operations. Real-time information on efficiency, combined with process optimisation and needs-based forecasting enables a well-managed district heating plant to make significant improvements in efficiency.
If a company has multiple production plants and the management has visibility into the real-time situation of each plant, the operations of the entire network can be regulated and optimised both in the short and long-term. Readings from district thermometers (which can be read remotely) can also be processed together with the real-time data to create predictions of customer behaviour. Similarly, data from weather forecasts can be easily obtained and processed as well.
When all these are combined into one Dashboard view, the company has the tools available to effectively manage emissions.
How can this be achieved?
While the above may seem like significant changes to the current situation, this is not the case. The lacking supply of biofuels in light of increasing demand poses a challenge to achieving the goals set, but significant measures are already being taken to improve production efficiency.
It is fully feasible to gather data with IoT-based solutions and further process it with, for example, the built-in machine learning features on the Microsoft Azure cloud platform.
There are also solutions to present all the mentioned data in one view.
By combining these methods with their new biofuel plant, Kemi Energy and Water Oy believes it will reduce its carbon dioxide emissions by a third. For instance, it is already nearing the goals that were set in the Paris Climate Agreement (2016).
