Existing smart grid security research investigates different attack techniques and cascading failures from the attackers’ viewpoints, while the defenders’ or the operators’ protection strategies are somehow neglected. Game theoretic methods are applied for the attacker–defender games in the smart grid security area. Yet, most of the existing works only use the one-shot game and do not consider the dynamic process of the electric power grid. In this paper, we propose a new solution for a multistage game (also called a dynamic game) between the attacker and the defender based on reinforcement learning to identify the optimal attack sequences given certain objectives (e.g., transmission line outages or generation loss). Different from a one-shot game, the attacker here learns a sequence of attack actions applying for the transmission lines and the defender protects a set of selected lines. After each time step, the cascading failure will be measured, and the line outage (and/or generation loss) will be used as the feedback for the attacker to generate the next action. The performance is evaluated on W&W 6-bus and IEEE 39-bus systems. A comparison between a multistage attack and a one-shot attack is conducted to show the significance of the multistage attack. Furthermore, different protection strategies are evaluated in simulation, which shows that the proposed reinforcement learning solution can identify optimal attack sequences under several attack objectives. It also indicates that attacker’s learned information helps the defender to enhance the security of the system.

A Multistage Game in Smart Grid Security: A Reinforcement Learning Solution

The introduction of smart meter technology is a great challenge for the German energy industry. It requires not only large investments in the communication and metering infrastructure, but also a redesign of traditional business processes. The newly incurring costs cannot be fully passed on to the end customers. One option to counterbalance these expenses is to exploit the newly generated smart metering data for the creation of new services and improved processes. For instance, performing a cluster analysis of smart metering data focused on the customers’ time-based consumption behavior allows for a detailed customer segmentation. In the article we present a cluster analysis performed on real-world consumption data from a smart meter project conducted by a German regional utilities company. We show how to integrate a cluster analysis approach into a business intelligence environment and evaluate this artifact as defined by design science. We discuss the results of the cluster analysis and highlight options to apply them to segment-specific tariff design.

/pdf/cluster-analysis-of-smart-metering-data-4munaboifx.pdf

Cluster Analysis of Smart Metering Data

Price-based demand response is applied to electric power systems. Demand elasticity and consumer response enables load reduction. The methodology is implemented in the DemSi demand response simulator. Competitive electricity markets have arisen as a result of power sector restructuration and power system deregulation. The players participating in competitive electricity markets must define strategies and make decisions using all the available information and business opportunities.

/pdf/demand-response-management-in-power-systems-using-particle-2xp904s545.pdf

Demand Response Management in Power Systems Using Particle Swarm Optimization

The power system worldwide is going through a revolutionary transformation due to the integration with various distributed components, including advanced metering infrastructure, communication infrastructure, distributed energy resources, and electric vehicles, to improve the reliability, energy efficiency, management, and security of the future power system These components are becoming more tightly integrated with IoT They are expected to generate a vast amount of data to support various applications in the smart grid, such as distributed energy management, generation forecasting, grid health monitoring, fault detection, home energy management, etc With these new components and information, artificial intelligence techniques can be applied to automate and further improve the performance of the smart grid In this paper, we provide a comprehensive review of the state-of-the-art artificial intelligence techniques to support various applications in a distributed smart grid In particular, we discuss how artificial techniques are applied to support the integration of renewable energy resources, the integration of energy storage systems, demand response, management of the grid and home energy, and security As the smart grid involves various actors, such as energy produces, markets, and consumers, we also discuss how artificial intelligence and market liberalization can potentially help to increase the overall social welfare of the grid Finally, we provide further research challenges for large-scale integration and orchestration of automated distributed devices to realize a truly smart grid

State-of-the-Art Artificial Intelligence Techniques for Distributed Smart Grids: A Review

Incorporating affective characteristics (such as personality, emotion, and mood) in an agent-based group decision-support system can help improve the negotiation process.

Personality, Emotion, and Mood in Agent-Based Group Decision Making

This Guest Editors' Introduction identifies an opportunity for the cross-fertilization between power systems and energy markets researchers and new developments of AI. The articles selected for this special issue provide the state-of-the-art information about research being conducted using AI in power systems and energy markets.

Chen-Ching Liu

Papers

AI in Power Systems and Energy Markets