The world is witnessing an unprecedented transformation in the energy sector, with the increasing adoption of renewable energy sources, the emergence of energy storage technologies, and the growing importance of distributed energy resources. This shift is in line with the Third and Fourth Industrial Revolution, a concept popularised by Jeremy Rifkin, Economist and Social theorist, which envisions a future where renewable energy and digital technologies drive a more decentralised, collaborative, and sustainable global economy.
At the heart of this transformation lies the need for advanced remote energy management solutions that can optimise the operation of energy sites, considering the dynamic nature of the energy market and the unique characteristics of each site. To play our part in enabling these sites to run optimally, Evergen are developing solutions that combine open source tools with advanced machine learning techniques and the concept of digital twins. This blog delves into our approach and how it aims to revolutionise the way remote sites are managed and optimised.
Smarter Energy Solutions
Traditionally, remote energy sites have been managed using Supervisory Control and Data Acquisition (SCADA) systems, providing basic control and monitoring capabilities. However, the energy market is becoming more complex and dynamic, and there is a growing need for advanced optimisation solutions that can take into account various factors, such as weather conditions, market information, and site-specific telemetry data.
To address this challenge, Evergen is developing a solution that integrates open source tools with machine learning algorithms to enable more sophisticated remote management and optimisation of energy sites. By focusing our intellectual property and value-added on the optimisation model, we aim to contribute to and leverage the open-source ecosystem as much as possible.
Our approach involves the use of industrial PCs running Ubuntu LTS, a widely adopted and supported Linux distribution. We deploy our software as Docker images, ensuring ease of installation, configuration, and updates. For telemetry gathering, we will be collecting data using Telegraf, a powerful open-source agent that can read Modbus, a common protocol used by utility sites. To facilitate secure communication between the site and our AWS IoT endpoints, we use MQTT, a lightweight messaging protocol designed for the Internet of Things.
In addition to leveraging open source tools for data collection and communication, we are also building digital twins of the remote energy sites. Digital twins are virtual representations of physical assets that can simulate the behaviour of their real-world counterparts. By creating digital twins, we can better understand the performance of each site and run simulations to explore different optimisation scenarios without affecting actual operations.
With our solution in place, we can gather real-time telemetry data from remote sites, collect market information, and obtain weather prediction models. Using machine learning algorithms, we can then analyse this data to determine the optimal way to manage the site, taking into account its generation and storage capabilities.
For sites with both generation and storage, our solution can intelligently decide when to buy, sell, or store energy, maximising profitability and sustainability. By helping these sites to operate more efficiently, we can contribute to the larger goal of orchestrating over 10GW of distributed energy assets worldwide, in line with Evergen’s mission.
The Role of Machine Learning
The role of machine learning in the transition to renewables cannot be overstated. By incorporating advanced algorithms, we can process vast amounts of data from multiple sources, identifying patterns and trends that would be impossible to detect manually. This enables us to make more informed decisions and to continuously improve our optimisation models as more data becomes available.
Moreover, our commitment to open-source technologies allows us to tap into a vast pool of collective knowledge and resources, fostering a collaborative environment where innovation thrives. By contributing to and building upon existing open-source projects, we can accelerate the development of our solution and stay at the forefront of cutting-edge advancements in the energy sector.
Innovation is Key
Innovative approaches to remote energy management, combining open-source tools, digital twins, and machine learning, represents a significant step forward in the pursuit of the Fourth Industrial Revolution. By enabling more efficient, sustainable, and intelligent operation of remote energy sites, we can contribute to the larger global effort to transition towards a more decentralised, collaborative, and renewable energy-driven economy.
As we continue to develop and refine our solution, we remain committed to sharing our progress and collaborating with the open-source community to drive further advancements in the energy sector. By working together, we can overcome the challenges of an increasingly complex energy market and unlock the full potential of distributed energy resources.
In the coming years, we expect to see a growing number of remote energy sites adopting advanced management and optimisation solutions like ours. The integration of digital twins, machine learning, and open-source tools will empower these sites to make smarter, data-driven decisions and better adapt to the ever-changing energy landscape.
Our vision is one of a more sustainable and interconnected energy future, where remote sites can seamlessly interact with the grid and each other, optimising their operations for maximum efficiency and minimum environmental impact. By harnessing the power of the Fourth Industrial Revolution, we can help bring this vision to life and usher in a new era of global energy innovation.