AI-based algorithms
for efficient design of energy systems
Intelligent generation, storing and consumption of renewable energies
The promotion of renewable energies is an integral part of international climate policy. But often the embedding of renewable energies into the existing structure of supply is a serious challenge.
The natural fluctuations of wind and solar energy have to be balanced by a flexible control of other generators, consumers or energy storage facilities.
Beyond the need of technical adjustments, new methods need to be applied to appropriately design the energy system.
Our algorithm
Qantic has developed an algorithm based on new techniques from the field of artificial intelligence. It is tailor-made for the optimization tasks in the energy industry. The algorithm can derive recommendations for the optimal system design. Essential elements are:
Neural network
An agent that is specialized in the design of complex energy systems and uses a deep artificial neural network (deep learning).
Learning method
A special learning method to train the agent to plan energy systems and coordinate generation, storing and consumption.
Simulation
A technically detailed and flexibly extendable simulation environment for energy systems in which the agent trains and adjusts its neural network in a self-learning procedure.
Design of energy systems with artificial intelligence
Our software Q-System models and valuates complex energy systems. All physical properties of the technologies (e.g. wind, solar, battery, conventional generation, co-generators) are simulated in detail. The algorithm determines the optimal operating point for all components and simulates the control of the energy system under all project specific constraints.
Particularly, the coordination of storage, conventional generation and other flexibilities is refined. Hereby, a higher part of renewable energies becomes usable. The components are operated at a higher efficiency and lower maintenance costs. Q-System also manages the reserves needed to compensate for fluctuations in grid load or component failures.
In consideration of all project related requirements, Q-System can determine the least-cost energy system from a multitude of alternative system designs and optimally scale all system components. The product is perfectly suited for the design of microgrids and decentralized energy systems for industry and commerce.
Q-System supports you with the following features:
- Detailed technical and economical model for a variety of generation and storage technologies
- Extensive capabilities to parameterize components
- High resolution dynamic simulation of system and components
- Detailed modeling of system’s reserve requirements, stochastic fluctuations and component failures
- Determining of optimal system design and component sizing
Your advantages at a glance
Costs and emissions can be reduced up to 30% by designing energy systems using our AI-based algorithm as compared to the use of conventional optimization techniques. Our AI-based algorithm is characterized by the following advantages:
The algorithm is suited to handle a high system complexity which typically arises when multiple distributed energy resources are conflated to an energy system. As compared to conventional methods a more detailed modeling is possible, which allows to draw on the full potential of all system components.
Big data and high resolution real-time information can be processed by the optimization engine. This makes it possible to extract relevant information in a self-learning manner and to derive accurate forecasts. Hence, Q-System considers the fluctuations of renewable energy sources very precisely and foresighted.
Smart energy solutions benefit from artificial intelligence
In parallel to the expansion of renewable energy sources, the energy system is increasingly affected by decentralization and digitalization. This creates new business models and energy supply systems. Besides applications in the conventional energy system, our solutions are particularly fitted for the business areas of the new energy world:
- Virtual power plants (VPP)
- Microgrids
- Smart city
- E-mobility / Smart charging
- Demand side management / Demand response