Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics-based swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. Also, it provides a comprehensive survey on the power system applications that have benefited from the powerful nature of PSO as an optimization technique. For each application, technical details that are required for applying PSO, such as its type, particle formulation (solution representation), and the most efficient fitness functions are also discussed.

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Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems

A power grid is a complex system connecting electric power generators to consumers through power transmission and distribution networks across a large geographical area. System monitoring is necessary to ensure the reliable operation of power grids, and state estimation is used in system monitoring to best estimate the power grid state through analysis of meter measurements and power system models. Various techniques have been developed to detect and identify bad measurements, including interacting bad measurements introduced by arbitrary, nonrandom causes. At first glance, it seems that these techniques can also defeat malicious measurements injected by attackers.In this article, we expose an unknown vulnerability of existing bad measurement detection algorithms by presenting and analyzing a new class of attacks, called false data injection attacks, against state estimation in electric power grids. Under the assumption that the attacker can access the current power system configuration information and manipulate the measurements of meters at physically protected locations such as substations, such attacks can introduce arbitrary errors into certain state variables without being detected by existing algorithms. Moreover, we look at two scenarios, where the attacker is either constrained to specific meters or limited in the resources required to compromise meters. We show that the attacker can systematically and efficiently construct attack vectors in both scenarios to change the results of state estimation in arbitrary ways. We also extend these attacks to generalized false data injection attacks, which can further increase the impact by exploiting measurement errors typically tolerated in state estimation. We demonstrate the success of these attacks through simulation using IEEE test systems, and also discuss the practicality of these attacks and the real-world constraints that limit their effectiveness.

False data injection attacks against state estimation in electric power grids

A power grid is a complex system connecting electric power generators to consumers through power transmission and distribution networks across a large geographical area. System monitoring is necessary to ensure the reliable operation of power grids, and state estimation is used in system monitoring to best estimate the power grid state through analysis of meter measurements and power system models. Various techniques have been developed to detect and identify bad measurements, including the interacting bad measurements introduced by arbitrary, non-random causes. At first glance, it seems that these techniques can also defeat malicious measurements injected by attackers.In this paper, we present a new class of attacks, called false data injection attacks, against state estimation in electric power grids. We show that an attacker can exploit the configuration of a power system to launch such attacks to successfully introduce arbitrary errors into certain state variables while bypassing existing techniques for bad measurement detection. Moreover, we look at two realistic attack scenarios, in which the attacker is either constrained to some specific meters (due to the physical protection of the meters), or limited in the resources required to compromise meters. We show that the attacker can systematically and efficiently construct attack vectors in both scenarios, which can not only change the results of state estimation, but also modify the results in arbitrary ways. We demonstrate the success of these attacks through simulation using IEEE test systems. Our results indicate that security protection of the electric power grid must be revisited when there are potentially malicious attacks.

The development of a trustworthy smart grid requires a deeper understanding of potential impacts resulting from successful cyber attacks. Estimating feasible attack impact requires an evaluation of the grid's dependency on its cyber infrastructure and its ability to tolerate potential failures. A further exploration of the cyber-physical relationships within the smart grid and a specific review of possible attack vectors is necessary to determine the adequacy of cybersecurity efforts. This paper highlights the significance of cyber infrastructure security in conjunction with power application security to prevent, mitigate, and tolerate cyber attacks. A layered approach is introduced to evaluating risk based on the security of both the physical power applications and the supporting cyber infrastructure. A classification is presented to highlight dependencies between the cyber-physical controls required to support the smart grid and the communication and computations that must be protected from cyber attack. The paper then presents current research efforts aimed at enhancing the smart grid's application and infrastructure security. Finally, current challenges are identified to facilitate future research efforts.

Cyber–Physical System Security for the Electric Power Grid

A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems

This paper discusses the state of the art in electric power system state estimation. Within energy management systems, state estimation is a key function for building a network real-time model. A real-time model is a quasi-static mathematical representation of the current conditions in an interconnected power network. This model is extracted at intervals from snapshots of real-time measurements (both analog and status). The new modeling needs associated with the introduction of new control devices and the changes induced by emerging energy markets are making state estimation and its related functions more important than ever.

Electric power system state estimation

Acknowledgments. Foreword. 1. Real-Time Modeling of Power Networks. 2. Least-Squares and Minimum Norm Problems. 3. DC State Estimator. 4. Power Flow Equations. 5. Network Reduction and Gauss Elimination. 6. Network Topology Processing. 7. Observability Analysis. 8. Basic Techniques for Bad Data Processing. 9. Multiple Bad Data Processing Techniques. 10. AC State Estimator. 11. Estimation Based on Multiple Scans of Measurements. 12. Fast Decoupled State Estimator. 13. Numerically Robust State Estimators. A: Statistical Properties of Estimated Quantities. B: Givens Rotation. Index.

State Estimation in Electric Power Systems: A Generalized Approach

This paper describes a mathematical framework for the solution of the economic dispatch problem with security constraints, which can take into account the system corrective capabilities after the outage has occurred. The solution algorithm is based on mathematical programming decomposition techniques that allow the iterative solution of a "base-case" economic dispatch and separate contingency analysis with generation rescheduling to eliminate constraint violations. The resulting dispatch has the same security level as the usual security-constrained dispatch, but with lower operating costs. Case studies with the IEEE 118-bus are presented. The extension of the methodology to the probabilistic case will be discussed in a forthcoming paper.

Security-Constrained Optimal Power Flow with Post-Contingency Corrective Rescheduling

An operationally "secure" power system is one with low probability of blackout or equipment damage. The power system control processes needed to maintain a designated security level at minimum operating cost are extremely complicated. They increasingly depend upon on-line computer security analysis and optimization. This on-line technology is still relatively new, with enormous further potential. Since security and optimality are normally conflicting requirements of power system control, it is inappropriate to treat them separately. Therefore, they are slowly becoming coalesced into a unified hierarchical mathematical problem formulation: one that is, however, far too complex to afford anything but an approximate, near-optimal solution. The practical validity of this unifying trend relies on being able to incorporate all significant security constraints within the process. The main two current computational tools in this field are contingency analysis and special operations-oriented versions of optimal power flow (OPF). Contingency analysis identifies potential emergencies through extensive "what if?." simulations on the power system network. OPF is a major extension to the conventional dispatch calculation. It can respect system static security limits, and can schedule reactive as well as active power. Moreover, the advanced versions of OPF include or interface with contingency analysis. This paper reviews present formulations and methods, and tries to point out areas of difficulty that constitute the main challenges for successful practical on-line implementations over the coming years.

Security analysis and optimization

The data of four networks that can be used in carrying out comparative studies with methods for transmission network expansion planning are given. These networks are of various types and different levels of complexity. The main mathematical formulations used in transmission expansion studies-transportation models, hybrid models, DC power flow models, and disjunctive models are also summarised and compared. The main algorithm families are reviewed-both analytical, combinatorial and heuristic approaches. Optimal solutions are not yet known for some of the four networks when more accurate models (e.g. the DC model) are used to represent the power flow equations-the state of the art with regard to this is also summarised. This should serve as a challenge to authors searching for new, more efficient methods.

A. Monticelli

Papers

Electric power system state estimation

State Estimation in Electric Power Systems: A Generalized Approach

Security-Constrained Optimal Power Flow with Post-Contingency Corrective Rescheduling

Security analysis and optimization

Test systems and mathematical models for transmission network expansion planning