The smart grid is arguably one of the most complex cyber-physical systems (CPS). Complex security challenges have been revealed in both the physical and the cyber parts of the smart grid, and an integrative analysis on the cyber-physical (CP) security is emerging. This paper provides a comprehensive and systematic review of the critical attack threats and defence strategies in the smart grid. We start this survey with an overview of the smart grid security from the CP perspective, and then focuses on prominent CP attack schemes with significant impact on the smart grid operation and corresponding defense solutions. With an in-depth review of the attacks and defences, we then discuss the opportunities and challenges along the smart grid CP security. We hope this paper raises awareness of the CP attack threats and defence strategies in complex CPS-based infrastructures such as the smart grid and inspires research effort toward the development of secure and resilient CP infrastructures.

Cyber-physical attacks and defences in the smart grid: a survey

Spot Pricing of Electricity

As the deployment of Internet of Things (IoT) is experiencing an exponential growth, it is no surprise that many recent cyber attacks are IoT-enabled : the attacker initially exploits some vulnerable IoT technology as a first step toward compromising a critical system that is connected, in some way, with the IoT. For some sectors, like industry, smart grids, transportation, and medical services, the significance of such attacks is obvious, since IoT technologies are part of critical back-end systems. However, in sectors where IoT is usually at the end-user side, like smart homes, such attacks can be underestimated, since not all possible attack paths are examined. In this paper, we survey IoT-enabled cyber attacks, found in all application domains since 2010. For each sector, we emphasize on the latest, verified IoT-enabled attacks, based on known real-world incidents and published proof-of-concept attacks. We methodologically analyze representative attacks that demonstrate direct, indirect, and subliminal attack paths against critical targets. Our goal is threefold: 1) to assess IoT-enabled cyber attacks in a risk-like approach, in order to demonstrate their current threat landscape; 2) to identify hidden and subliminal IoT-enabled attack paths against critical infrastructures and services; and 3) to examine mitigation strategies for all application domains.

A Survey of IoT-Enabled Cyberattacks: Assessing Attack Paths to Critical Infrastructures and Services

The notion of secure operation of power systems, with its present semantic, dates back to the last decade. Since then, tremendous research effort has investigated secure operation of the power system. Nevertheless, operators are still faced with security issues, with even larger uncertainty, however, caused by the integration of renewable energy sources (RES). The system's ability to cope with the volatility of RES and load becomes a critical issue in power system operation. This paper surveys the literature on the concepts of power system flexibility, indices of flexibility, and implementation of the concept of flexibility in power system security. The paper proceeds to review the origin of the reserve problem, the meaning of reserve, its technical classification and related economical aspects. The paper highlights the effect of renewables on these aspects, and suggests new research directions.

A Review of Power System Flexibility With High Penetration of Renewables

This paper proposes a day-ahead stochastic scheduling model in electricity markets. The model considers hourly forecast errors of system loads and variable renewable sources as well as random outages of power system components. A chance-constrained stochastic programming formulation with economic and reliability metrics is presented for the day-ahead scheduling. Reserve requirements and line flow limits are formulated as chance constraints in which power system reliability requirements are to be satisfied with a presumed level of high probability. The chance-constrained stochastic programming formulation is converted into a linear deterministic problem and a decomposition-based method is utilized to solve the day-ahead scheduling problem. Numerical tests are performed and the results are analyzed for a modified 31-bus system and an IEEE 118-bus system. The results show the viability of the proposed formulation for the day-ahead stochastic scheduling. Comparative evaluations of the proposed chance-constrained method and the Monte Carlo simulation (MCS) method are presented in the paper.

Chance-Constrained Day-Ahead Scheduling in Stochastic Power System Operation

With increasing renewable energy resources, price- sensitive loads, and electric-vehicle charging stations in the power grid, uncertainties on both power generation and consumption sides become critical factors in the Security-Constrained Unit Commitment (SCUC) problem. Recently, worst scenario based robust optimization approaches are employed to consider uncertainties. This paper proposes a non-conservative robust SCUC model and an effective solution approach. The contributions of this paper are three-fold. First, the commitment and dispatch solution obtained in this paper can be directly used in day-ahead market as it overcomes two issues, conservativeness and absence of robust dispatch, which are the two largest obstacles to applying robust SCUC in real markets. Secondly, a new concept recourse cost requirement, similar to reserve requirement, is proposed to define the upper bound of re-dispatch cost when uncertainties are revealed. Thirdly, a novel decomposition approach is proposed to effectively address the well-known computational challenge in robust approaches. Simulation results on the IEEE 118-bus system validate the effectiveness of the proposed novel model and solution approach.

Robust Security-Constrained Unit Commitment and Dispatch With Recourse Cost Requirement

The increasing penetration of renewable energy in recent years has led to more uncertainties in power systems. These uncertainties have to be accommodated by flexible resources (i.e., upward and downward generation reserves). In this paper, a novel concept, Uncertainty Marginal Price (UMP), is proposed to price both the uncertainty and generation reserve. At the same time, the energy is priced at Locational Marginal Price (LMP). A novel market clearing mechanism is proposed to credit the generation and reserve and to charge the load and uncertainty within the robust unit commitment in the day-ahead market. We derive the UMPs and LMPs in the robust optimization framework. UMP helps allocate the cost of generation reserves to uncertainty sources. We prove that the proposed market clearing mechanism leads to partial market equilibrium. We find that transmission reserves must be kept explicitly in addition to generation reserves for uncertainty accommodation. We prove that transmission reserves for ramping delivery may lead to Financial Transmission Right (FTR) underfunding in existing markets. The FTR underfunding can be covered by congestion fund collected from uncertainty payment in the proposed market clearing mechanism. Simulations on a six-bus system and the IEEE 118-bus system are performed to illustrate the new concepts and the market clearing mechanism.

Uncertainty Marginal Price, Transmission Reserve, and Day-Ahead Market Clearing With Robust Unit Commitment

Two critical issues have arisen in transmission expansion planning with the rapid growth of wind power generation. First, severe power ramping events in daily operation due to the high variability of wind power generation pose great challenges to multi-year planning decision making. Second, the long construction periods of transmission lines may not be able to keep pace with the fast growing uncertainty due to the increasing integration of wind power generation. To address such issues, we propose a comprehensive robust planning model considering different resources, namely, transmission lines, generators, and FACTS devices. Various factors are taken into account, including flexibility requirements, construction period, and cost. We construct the hourly net load ramping uncertainty (HLRU) set to characterize the variation of hourly net load including wind power generation, and the annual net load duration curve uncertainty (LDCU) set for the uncertainty of normal annual net load duration curve. This results in a two-stage robust optimization model with two different types of uncertainty sets, which are decoupled into two different sets of subproblems to make the entire solution process tractable. Numerical simulations with real-world data show that the proposed model and solution method are effective in coordinating different flexible resources and rendering robust expansion planning strategies.

Robust Coordinated Transmission and Generation Expansion Planning Considering Ramping Requirements and Construction Periods

Two critical issues have arisen in transmission expansion planning with the rapid growth of wind power generation. First, severe power ramping events in daily operation due to the high variability of wind power generation pose great challenges to multi-year planning decision making. Second, the long construction periods of transmission lines may not be able to keep pace with the fast growing uncertainty due to the increasing integration of renewable energy generation. To address such issues, we propose a comprehensive robust planning model considering different resources, namely, transmission lines, generators, and FACTS devices. Various factors are taken into account, including flexibility requirement, construction period, and cost. We construct the hourly net load ramping uncertainty (HLRU) set to characterize the variation of hourly net load including wind power generation, and the annual net load duration curve uncertainty (LDCU) set for the uncertainty of normal annual net load duration curve. This results in a two-stage robust optimization model with two different types of uncertainty sets, which are decoupled into two different sets of subproblems to make the entire solution process tractable. Numerical simulations with real-world data show that the proposed model and solution method are effective to coordinate different flexible resources, rendering robust expansion planning strategies.

The potential economic impact of transmission line rating (TLR) attacks in two-settlement electricity markets is studied in this paper. We show that nodal prices in real-time markets can be manipulated via a TLR attack, which can be modeled as a bi-level optimization problem. Several acceleration techniques are developed to reduce the computational burden of solving the bi-level problem. A heuristic strategy is proposed to deal with the issue of multiplicity in pricing. The uncertainties in load are also considered in the proposed TLR model. Numerical simulations demonstrate that well-designed TLR attacks can manipulate the profits of market participants in the two-settlement markets. Benchmark testing shows that the proposed acceleration techniques can reduce computation time tremendously, and the proposed heuristic strategy can mitigate the issue of multiplicity in pricing.

Hongxing Ye

Papers

Robust Security-Constrained Unit Commitment and Dispatch With Recourse Cost Requirement

Uncertainty Marginal Price, Transmission Reserve, and Day-Ahead Market Clearing With Robust Unit Commitment

Robust Coordinated Transmission and Generation Expansion Planning Considering Ramping Requirements and Construction Periods

Robust Coordinated Transmission and Generation Expansion Planning Considering Ramping Requirements and Construction Periods

Transmission Line Rating Attack in Two-Settlement Electricity Markets