Efficient design and operation 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 and control the energy system. New control algorithms have to be developed and applied in parallel to a full digitalization of processes.

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 handle demanding control operations and derive recommendations for the optimal system design. Essential elements are:

Neural network

An agent that is specialized in the control of complex energy systems and uses a deep artificial neural network (deep learning).

Learning method

A special learning method to train the agent to control 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

Reducing costs and emissions by operating energy systems with artificial intelligence

The intelligent coordination of generation, storing and consumption of energy is a key to a low carbon and low cost energy supply. Besides the high technical complexity, it has to be coped with an increasing volume of measurement data and real-time information. Actual values for market data or grid operation often also need to be included into the optimal system control.

Our customizable AI-based solutions help to extract the relevant information from large data streams and derive accurate forecasts of energy flows. The optimal system control can be calculated near real-time for a variety of distributed energy resources, considering complex technical, legal and economic constraints. The natural fluctuations of wind and solar power can be balanced immediately at lowest costs. The necessary reserves for a secure operation are dynamically determined and adjusted.

Based on Q-System we support you with customizable solutions to efficiently operate your energy facilities:

  • Detailed technical and economical model of your energy systems, reflecting complex project-specific constraints
  • Ongoing analysis of real-time data on demand, generation, weather forecasts, market prices and other information
  • Modeling of stochastic deviations of consumption and generation with powerful predictive analytics
  • Detailed simulation of aging and wear of components
  • Dynamic optimization of the operation points of components for an efficient system control

Your advantages at a glance

Costs and emissions can be reduced up to 30% by designing and operating 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:

Short calculation time enables the usage in challenging real-time-applications. This allows for a very short term balancing of fluctuations in renewable generation. High frequency data streams from grid operators and trading platforms (e.g. intraday or peer-to-peer) can be processed in the optimization.

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