Security Risk Assessment of Cyber Physical Power System Based on Rough Set and Gene Expression Programming

Risk assessment is essential for the safe and reliable operation of cyber physical power system. Traditional security risk assessment methods do not take integration of cyber system and physical system of power grid into account. In order to solve this problem, security risk assessment algorithm of cyber physical power system based on rough set and gene expression programming is proposed. Firstly, fast attribution reduction based on binary search algorithm is presented. Secondly, security risk assessment function for cyber physical power system is mined based on gene expression programming. Lastly, security risk levels of cyber physical power system are predicted and analyzed by the above function model. Experimental results show that security risk assessment function model based on the proposed algorithm has high efficiency of function mining, accuracy of security risk level prediction and strong practicality. © 2014 Chinese Association of Automation.

Uplifting the complexity of analysis for probabilistic security of electricity supply assessments using artificial neural networks

The energy sector faces rapid decarbonisation and decision-makers demand reliable assessments of the security of electricity supply. For this, detailed simulation models with a high temporal and technological resolution are required. When confronted with increasing weather-dependent renewable energy generation, probabilistic simulation models have proven. The significant computational costs of calculating a scenario, however, limit the complexity of further analysis. Advances in code optimization as well as the use of computing clusters still lead to runtimes of up to eight hours per scenario. However ongoing research highlights that tailor-made approximations are potentially the key factor in further reducing computing time. Consequently, current research aims to provide a method for the rapid prediction of widely varying scenarios. In this work artificial neural networks (ANN) are trained and compared to approximate the system behavior of the probabilistic simulation model. To do so, information needs to be sampled from the probabilistic simulation in an efficient way. Because only a limited space in the whole design space of the 16 independent variables is of interest, a classification is developed. Finally it required only around 35 min to create the regression models, including sampling the design space, simulating the training data and training the ANNs. The resulting ANNs are able to predict all scenarios within the validity range of the regression model with a coefficient of determination of over 0.9998 for independent test data (1.051.200 data points). They need only a few milliseconds to predict one scenario, enabling in-depth analysis in a brief period of time.

Primary energy congestion of power systems

Primary energy has the potential to bring challenges to the reliability,economic,and eco-friendliness of global electric power systems.The concept of electric power security are proposed,including many factors that are not considered in power system reliability analysis,such as coal supply for power system,fuel price for electricity power market,carbon emissions of power generation,and so on.It is broader than power system reliability and security,which means that providing energy in the form of reliable,economical,and eco-friendly electric power.Following an account of existing blackout defence methods and systems,the processes of transforming various kinds of primary energy into electric power and the interactions between them are described,particularly with regard to flows of energy,capital,and information.Factors that limit the liquidity of these flows are defined as“generalized congestion”,and the challenges presented by primary energy are named“primary energy congestion”.China’s dilemma on coal supply for power generation is presented in some detail as an example of primary energy congestion.To cope with impacts of the primary energy on electric power systems,an idea is introduced to extend the scope of existing coordinated blackout defence systems to account for primary energy,which would lead to an integrated tool to provide decision support to power system operators accounting for primary energy congestion.