Especially during the summer period, public and private forests are threatened by wildfires. Wildfires are among the most common forms of natural disaster in some regions and can cause damage to property and human life. Moreover, in ecosystems where they are uncommon or where non-native vegetation has encroached, they may have strongly negative ecological effects.
Wildfires may be characterized by a rapid forward rate of spread when burning through dense uninterrupted fuels and can move as fast as more than 10 km/h (6.5 mph) in forests and 22 kilometers per hour (14 mph) in grasslands. Wildfires can advance tangential to the main front to form a flanking front or burn in the opposite direction of the main front by backing.
To this end, strong efforts have been made to avoid or mitigate such consequences by early fire detection or fire risk mapping. Traditionally, forest fires were mainly detected by human observation from fire lookout towers, however, this approach is inefficient, as it is prone to human error and fatigue. On the other hand, conventional sensors for the detection of heat, smoke, flame, and gas typically take time for the particles to reach the point of sensors and activate them. In addition, the range of such sensors is relatively small, hence, a large number of sensors need to be installed to cover large areas. It is therefore necessary to implement fire prevention systems allowing ultra-early fire detection, ideally even before flames start spreading, for a prompt intervention of fire brigades.
In this scenario, leveraging on the presence of renewable power plants close to the forests is key. The objective is to exploit the sharing of the infrastructure built for the construction and operation of the plants by local communities in order to achieve fire prevention in the surrounding forest areas by utilizing innovative technological solutions that allow the development of fires outside the power plants (in neighboring areas - a few kilometers away) to be predicted or, at least, detected.
The aim of this initiative is to exploit new digital services with high added value to support local agronomic and tourism initiatives as well as to offer an improvement in the safety of the territory in terms of environmental/ fire risks, all this by sharing the enabling infrastructures with the communities. The final goal is the prevention of wildfires affecting public and private forests. The renewable power plants are not only a source of clean energy but could therefore also provide many additional services to the local communities, including wildfire prevention.
Enel Green Power is looking for an economical solution for ultra-early fire detection for forests, to be installed in the renewable power plants areas (i.e., wind and photovoltaic). The system should also be able to provide early detection of fires within the power plant.
Submissions should address the following Solution Requirements.
The proposed solutions must:
- Be installed on wind turbine generators or on photovoltaic generators (as opposed to the surrounding area)
- Be able to provide ultra-early (i.e., within 60 minutes, during smoldering phase/incipient/fire growth phase) fire detection for forests (within a radius of 10 kilometers from the power plant)
- Be able to provide ultra-early fire detection for renewable power plants’ components
- Be able to communicate with a central system via a wireless network
- Technology readiness level (TRL) ≥ 7.
The solution can also make use of Artificial Intelligence features and use different sources of data, also external from the power plant, in order to improve the detection accuracy.
The submitted proposal should include the following:
- Detailed description of the proposed device and/or technique, including (but not limited to):
- advantages and weaknesses of the proposed solution;
- information about the accuracy in the fire detection in terms of distance and response time;
- information about proposed solution applicability (i.e., type of areas, weather conditions, type of installation);
- information about costs (i.e., components, maintenance).
- Data, case studies, patent and journal references or any additional material that supports the proposed solution.
The proposal should not include any personal identifying information (name, username, company, address, phone, email, personal website, resume, etc.) or any information the Solvers may consider as their Intellectual Property they do not want to share.