Biblio

We present a privacy-preserving distributed reinforcement learning-based control scheme to address the problem of frequency control and economic dispatch in power generation systems. The proposed control approach requires neither a priori system model knowledge nor the mathematical formulation of the generation cost functions. Due to not requiring the generation cost models, the control scheme is capable of dealing with scenarios in which the cost functions are hard to formulate and/or non-convex. Furthermore, it is privacy-preserving, i.e. none of the units in the network needs to communicate its cost function and/or control policy to its neighbors. To realize this, we propose an actor-critic algorithm with function approximation in which the actor step is performed individually by each unit with no need to infer the policies of others. Moreover, in the critic step each generation unit shares its estimate of the local measurements and the estimate of its cost function with the neighbors, and via performing a consensus algorithm, a consensual estimate is achieved. The performance of our proposed control scheme, in terms of minimizing the overall cost while persistently fulfilling the demand and fast reaction and convergence of our distributed algorithm, is demonstrated on a benchmark case study.

This paper sheds light on the collaborative efforts in restoring cyber and physical subsystems of a modern power distribution system after the occurrence of an extreme weather event. The extensive cyber-physical interdependencies in the operation of power distribution systems are first introduced for investigating the functionality loss of each subsystem when the dependent subsystem suffers disruptions. A resilience index is then proposed for measuring the effectiveness of restoration activities in terms of restoration rapidity. After modeling operators' decision making for economic dispatch as a second-order cone programming problem, this paper proposes a heuristic approach for prioritizing the activities for restoring both cyber and physical subsystems. In particular, the proposed heuristic approach takes into consideration of cyber-physical interdependencies for improving the operation performance. Case studies are also conducted to validate the collaborative restoration model in the 33-bus power distribution system.

As power grid control systems become increasingly automated and distributed, security has become a significant design concern. Systems increasingly expose new avenues, at a variety of levels, for attackers to exploit and enable widespread disruptions and/or surveillance. Much prior work has explored the implications of attack models focused on false data injection at the front-end of the control system (i.e. during state estimation) [1]. Instead, in this paper we focus on characterizing the inherent cyber-attack vulnerabilities with power flow. Power flow (and power flow constraints) are at the core of many applications critical to operation of power grids (e.g. state estimation, economic dispatch, contingency analysis, etc.). We propose two algorithmic approaches for characterizing the vulnerability of buses within power grids to cyber-attacks. Specifically, we focus on measuring the instability of power flow to attacks which manifest as either voltage or power related errors. Our results show that attacks manifesting as voltage errors are an order of magnitude more likely to cause instability than attacks manifesting as power related errors (and 5x more likely for state estimation as compared to power flow).

Power grid operations rely on the trustworthy operation of critical control center functionalities, including the so-called Economic Dispatch (ED) problem. The ED problem is a large-scale optimization problem that is periodically solved by the system operator to ensure the balance of supply and load while maintaining reliability constraints. In this paper, we propose a semantics-based attack generation and implementation approach to study the security of the ED problem.1 Firstly, we generate optimal attack vectors to transmission line ratings to induce maximum congestion in the critical lines, resulting in the violation of capacity limits. We formulate a bilevel optimization problem in which the attacker chooses manipulations of line capacity ratings to maximinimize the percentage line capacity violations under linear power flows. We reformulate the bilevel problem as a mixed integer linear program that can be solved efficiently. Secondly, we describe how the optimal attack vectors can be implemented in commercial energy management systems (EMSs). The attack explores the dynamic memory space of the EMS, and replaces the true line capacity ratings stored in data regions with the optimal attack vectors. In contrast to the well-known false data injection attacks to control systems that require compromising distributed sensors, our approach directly implements attacks to the control center server. Our experimental results on benchmark power systems and five widely utilized EMSs show the practical feasibility of our attack generation and implementation approach.

Power grid operations rely on the trustworthy operation of critical control center functionalities, including the so-called Economic Dispatch (ED) problem. The ED problem is a large-scale optimization problem that is periodically solved by the system operator to ensure the balance of supply and load while maintaining reliability constraints. In this paper, we propose a semantics-based attack generation and implementation approach to study the security of the ED problem.1 Firstly, we generate optimal attack vectors to transmission line ratings to induce maximum congestion in the critical lines, resulting in the violation of capacity limits. We formulate a bilevel optimization problem in which the attacker chooses manipulations of line capacity ratings to maximinimize the percentage line capacity violations under linear power flows. We reformulate the bilevel problem as a mixed integer linear program that can be solved efficiently. Secondly, we describe how the optimal attack vectors can be implemented in commercial energy management systems (EMSs). The attack explores the dynamic memory space of the EMS, and replaces the true line capacity ratings stored in data regions with the optimal attack vectors. In contrast to the well-known false data injection attacks to control systems that require compromising distributed sensors, our approach directly implements attacks to the control center server. Our experimental results on benchmark power systems and five widely utilized EMSs show the practical feasibility of our attack generation and implementation approach.

Power grid operations rely on the trustworthy operation of critical control center functionalities, including the so-called Economic Dispatch (ED) problem. The ED problem is a large-scale optimization problem that is periodically solved by the system operator to ensure the balance of supply and load while maintaining reliability constraints. In this paper, we propose a semantics-based attack generation and implementation approach to study the security of the ED problem.1 Firstly, we generate optimal attack vectors to transmission line ratings to induce maximum congestion in the critical lines, resulting in the violation of capacity limits. We formulate a bilevel optimization problem in which the attacker chooses manipulations of line capacity ratings to maximinimize the percentage line capacity violations under linear power flows. We reformulate the bilevel problem as a mixed integer linear program that can be solved efficiently. Secondly, we describe how the optimal attack vectors can be implemented in commercial energy management systems (EMSs). The attack explores the dynamic memory space of the EMS, and replaces the true line capacity ratings stored in data regions with the optimal attack vectors. In contrast to the well-known false data injection attacks to control systems that require compromising distributed sensors, our approach directly implements attacks to the control center server. Our experimental results on benchmark power systems and five widely utilized EMSs show the practical feasibility of our attack generation and implementation approach.

The inherent heterogeneity in the uncertainty of variable generations (e.g., wind, solar, tidal and wave-power) in electric grid coupled with the dynamic nature of distributed architecture of sub-systems, and the need for information synchronization has made the problem of resource allocation and monitoring a tremendous challenge for the next-generation smart grid. Unfortunately, the deployment of distributed algorithms across micro grids have been overlooked in the electric grid sector. In particular, centralized methods for managing resources and data may not be sufficient to monitor a complex electric grid. This paper discusses a decentralized constrained decomposition using Linear Programming (LP) that optimizes the inter-area transfer across micro grids that reduces total generation cost for the grid. A test grid of IEEE 14-bus system is sectioned into two and three areas, and its effect on inter-transfer is analyzed.