Bad Data Injection Attack and Defense in Electricity Market using Game Theory Study

Applications of cyber technologies improve the quality of monitoring and decision making in smart grid. These cyber technologies are vulnerable to malicious attacks, and compromising them can have serious technical and economical problems. This paper specifies the effect of compromising each measurement on the price of electricity, so that the attacker is able to change the prices in the desired direction (increasing or decreasing). Attacking and defending all measurements are impossible for the attacker and defender, respectively. This situation is modeled as a zero sum game between the attacker and defender. The game defines the proportion of times that the attacker and defender like to attack and defend different measurements, respectively. From the simulation results based on the PJM 5 Bus test system, we can show the effectiveness and properties of the studied game.Comment: To appear in IEEE Transactions on Smart Grid, Special Issue on Cyber, Physical, and System Security for Smart Gri

Towards Intelligent Distribution Systems: Solutions for Congestion Forecast and Dynamic State Estimation Based Protection

The electrical distribution systems are undergoing drastic changes such as increasing penetration level of distributed renewable energy sources, energy storage, electrification of energy-efficient loads such as heat pumps and electric vehicles, etc., since the last decade, and more changes are expected in the future. These changes pose challenges for the distribution system operators such as increased level of network congestions, voltage variations, as well as protection settings and coordination, etc. These will require the development of new paradigms to operate distribution systems securely, safely, and economically while hosting a large amount of renewable energy sources.First, the thesis proposed a comprehensive assessment framework to assess the distribution system operator’s future-readiness and support them in determining the current status of their network infrastructures, business models, and policies and thus to identify areas for required developments. The analysis for the future-readiness of the three distribution system operators (from France, The Netherlands, and Sweden) using the proposed assessment framework has shown that presently the distribution system operators have a rather small penetration of renewable energy sources in their grids, however, which is expected to increase in the future. The distribution system operators would need investments in flexibilities, novel forecasting techniques, advanced grid control as well as improved protection schemes. The need for the development of new business models for customers and changes in the policy and regulations are also suggested by the analysis. Second, the thesis developed a congestion forecast tool that would support the distribution system operators to forecast and visualize network overloading and voltage variations issues for multiple forecasting horizons ranging from close-to-real time to day-ahead. The tool is based on a probabilistic power flow that incorporates forecasts of production from solar photovoltaic and electricity demand combined with load models along with the consideration of different operating modes of solar photovoltaic inverters to enhance the accuracy. The congestion forecast tool can be integrated into the existing distribution management systems of distribution system operators via an open cross-platform using Codex Smart Edge technology of Atos Worldgrid. The congestion forecast tool has been used in a case study for two real distribution systems (7-bus feeder and 141-bus system). It was demonstrated in the case study that the tool can predict the congestion in the networks with various prediction horizons. The congestion forecast tool would support distribution system operators by forecasting the network congestion and setting up a congestion management plan.Finally, the dynamic state estimation based protection scheme supported by advanced measurement technologies developed within EU project UNITED-GRID has been implemented and validated experimentally at Chalmers power system laboratory. This dynamic state estimation based protection scheme has a strong advantage over the traditional protection scheme as it does not require any relay settings and coordination which can overcome the protection challenges arising in distribution grids with a large amount of renewable energy sources. The results from the validation of the dynamic state estimation based protection scheme at Chalmers laboratory have shown that the fault detection using this scheme has worked properly as expected for an application of the line protection

Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems

Forecasting Recharging Demand to Integrate Electric Vehicle Fleets in Smart Grids

Electric vehicle fleets and smart grids are two growing technologies. These technologies provided new possibilities to reduce pollution and increase energy efficiency. In this sense, electric vehicles are used as mobile loads in the power grid. A distributed charging prioritization methodology is proposed in this paper. The solution is based on the concept of virtual power plants and the usage of evolutionary computation algorithms. Additionally, the comparison of several evolutionary algorithms, genetic algorithm, genetic algorithm with evolution control, particle swarm optimization, and hybrid solution are shown in order to evaluate the proposed architecture. The proposed solution is presented to prevent the overload of the power grid