Towards automated engineering and validation of cyber-physical energy systems

Abstract

The massive deployment of distributed generators from renewable sources in recent years has led to a fundamental paradigm change in terms of planning and operation of the electric power system. The usage of advanced automation and information and communication technology is a key element to handle these new challenges and to turn the traditional power system into a smart grid. The implementation of such complex systems solutions is associated with increasing development complexity resulting in increased engineering costs. The traditional engineering methods used for power system automation were not intended to be used for applications of this scale and complexity. However, the usage of proper methods, automation architectures, and corresponding tools holds huge optimization potential for the engineering process. Therefore, this work presents a model-based engineering and validation support system, covering the overall engineering process for smart grid applications.

Sustainable Electrification—Advances and Challenges in Electrical-Distribution Networks: A Review

“…Automation in the smart grid is a crucial aspect of modern-electrical-network infrastructure. It involves the integration of advanced technology such as automation systems, communication protocols, and control functions to enhance the efficiency, reliability, and security of the network [30]. One key component of automation in the smart grid is the implementation of control functions.…”

“…Systems-control engineering plays a fundamental role in designing and optimizing control strategies for smart grid To optimize smart grid power systems, predictive models are needed for demand forecasting and supply and demand analysis, and optimization techniques have been widely used to address the challenges of integrating uncertain renewable energy sources. Power-system deregulation and random-load fluctuations, methods used to predict the volatility of financial markets, can also be used to predict the volatility of environmental and load demands highlighting the importance of predictive modeling in this context [30].…”