Cyber attack-defense analysis for automatic generation control with renewable energy sources

The advancements in the power grid due to integration of new technology arises concerns regarding its reliability in terms of performance and security. On one hand, the gradual shift towards renewable energy sources leads to rise in uncertainty in terms of control and demand satisfaction. On the other hand, the integration of communication devices in order to make the grid smart increases its vulnerability to malicious activity. Automatic Generation Control (AGC), which is needed to maintain the system frequency and inter-area exchange, has an important priority in both the concerns. With rising shares of renewables and retiring of fossil-fuel based generation, a grid almost entirely served by renewables is a highly possible scenario. In such cases, the impact of an attack on the grid is likely to be influenced by the effect of renewables. Although previous research conducted crucial studies of malicious cyber events on the power grid, analysis in the presence of renewables is still at a nascent stage. As a contribution, this thesis presents an attack-defense analysis on the AGC operation of the power grid under varying conditions of renewables. It has two main contributions – determination of the influence of renewables during an attack and development of an effective algorithm for defense. First, this thesis discusses a cyber-attack on the AGC algorithm with various levels of renewable penetration, to analyze the effect of an attack with renewables. The results confirm that the impact of a cyber-attack will be increasingly aggravated by displacement of conventional generation with renewables. Then an algorithm for AGC using a PID based approach aimed at reducing the impact is proposed. From the experiments, the proposed algorithm is shown to reduce the impact of the attack. Secondly, an algorithm for attack mitigation is designed and its performance is analyzed for both the AGC algorithms. The various factors tested are its effectiveness in reducing the impact on the system, and its adverse effects on AGC operation during normal conditions and contingency response. The results show that the algorithm could mitigate the attack without having a negative impact on normal AGC operation. The contingency response analysis shows that during events resulting in a significant change in generation-load balance, the response could be adversely affected by the mitigation. The experiments were conducted using the Power Cyber CPS security testbed at Iowa State University. The thesis further briefly discusses the prospective research considering various developments in the power grid, renewables and attack vectors

Clustering-based negotiation profiles definition for local energy transactions

Electricity markets are complex and dynamic environments, mostly due to the large scale integration of renewable energy sources in the system. Negotiation in these markets is a significant challenge, especially when considering negotiations at the local level (e.g., between buildings and distributed energy resources). It is essential for a negotiator to be able to identify the negotiation profile of the players with whom he is negotiating. If a negotiator knows these profiles, it is possible to adapt the negotiation strategy and get better results in a negotiation. In order to identify and define such negotiation profiles, a clustering process is proposed in this paper. The clustering process is performed using the kml-k-means algorithm, in which several negotiation approaches are evaluated in order to identify and define players' negotiation profiles. A case study is presented, using as input data, information from proposals made during a set of negotiations. Results show that the proposed approach is able to identify players' negotiation profiles used in bilateral negotiations in electricity markets.This work has been developed under the CONTEST project - SAICTPOL/23575/2016 and has received funding from UID/EEA/00760/2013, funded by FEDER Funds through COMPETE program and by National Funds through FCT.info:eu-repo/semantics/publishedVersio

Real-time estimation and damping of SSR in a VSC-HVDC connected series-compensated system

Infrastructure reinforcement using high-voltage direct-current (HVDC) links and series compensation has been proposed to boost the power transmission capacity of existing ac grids. However, deployment of series capacitors may lead to subsynchronous resonance (SSR). Besides providing bulk power transfer, voltage source converter (VSC)-based HVDC links can be effectively used to damp SSR. To this end, this paper presents a method for the real-time estimation of the subsynchronous frequency component present in series-compensated transmission lines-key information required for the optimal design of damping controllers. A state-space representation has been formulated and an eigenvalue analysis has been performed to evaluate the impact of a VSC-HVDC link on the torsional modes of nearby connected thermal generation plants. Furthermore, the series-compensated system has been implemented in a real-time digital simulator and connected to a VSC-HVDC scaled-down test-rig to perform hardware-in-the-loop tests. The efficacy and operational performance of the ac/dc network while providing SSR damping is tested through a series of experiments. The proposed estimation and damping method shows a good performance both in time-domain simulations and laboratory experiments

False data injection attack detection in smart grid

Smart grid is a distributed and autonomous energy delivery infrastructure that constantly monitors the operational state of its overall network using smart techniques and state estimation. State estimation is a powerful technique that is used to determine the overall operational state of the system based on a limited set of measurements collected through metering systems. Cyber-attacks pose serious risks to a smart grid state estimation that can cause disruptions and power outages resulting in huge economical losses and are therefore a big concern to a reliable national grid operation. False data injection attacks (FDIAs), engineered on the basis of the knowledge of the network configuration, are difficult to detect using the traditional data detection mechanisms. These detection schemes have been found vulnerable and failed to detect these FDIAs. FDIAs specifically target the state data and can manipulate the state measurements in such a way that these false measurements appear real to the main control systems. This research work explores the possibility of FDIA detection using state estimation in a distributed and partitioned smart grid. In order to detect FDIAs we use measurements for residual-based testing which creates an objective function; and the probability of erroneous data is determined from this residual test. In this test, a preset threshold is determined based on the prior history of the state data. FDIA cases are simulated within a smart grid considering that the Chi-square detection state estimator fails in identifying such attacks. We compute the objective function using the standard weighted least problem and then test the objective function against the value in the Chi-square table. The gain matrix and the Jacobian matrix are computed. The state variables are computed in the form of a voltage magnitude. The state variables are computed after the inception of an attack to assess these state magnitude results. Different sizes of partitioning are used to improve the overall sensitivity of the Chi-square results. Our additional estimator is based on a Kalman estimation that consists of the state prediction and state correction steps. In the first step, it obtains the state and matrix covariance prediction, and in the second step, it calculates the Kalman gain and the state and matrix covariance update steps. The set of points is created for the state vector x at a time instant t. The initial vector and covariance matrix are based on a priori knowledge of the historical estimates. A set of sigma points is estimated by the state update function. Sigma points refer to the minimal set of sampling points that are selected and transformed using nonlinear function, and the new mean and the covariance are formed out of these transformed points. The idea behind this is that it is easier to compute a Gaussian distribution than an arbitrary nonlinear function. The filter gain, the mean and the covariance are used to estimate the next state. Our simulation results show that the combination of Kalman estimation and distributed state estimation improves the overall stability index and vulnerability assessment score of the smart grid. We built a stability index table for a smart grid based on the state estimates value after the inception of an FDIA. The vulnerability assessment score of the smart grid is based on common vulnerability scoring system (CVSS) and state estimates under the influence of an FDIA. The simulations are conducted in the MATPOWER program and different electrical bus systems such as IEEE 14, 30, 39, 118 and 300 are tested. All the contributions have been published in reputable journals and conferences.Doctor of Philosoph

Stability Analysis and Control Design in Smart Grid with Renewable Integration and Topology Control

The increasing penetration of variable energy resources such as wind and solar is changing the dynamic characteristics of power systems. As the majority of variable energy resources are asynchronously connected to the grid through power converter based generators (CBGs), power system stability is affected significantly by this new type of generator and new stability issues are emerging. Meanwhile, the flexible operation of future power grids, especially the active transmission topology control (TTC) will introduce frequent disturbances to the system and pose threats to power system stability. With the aforementioned background, the first aim of this dissertation is to analyze the impact of CBGs and TTC on power system stability while the second aim is to design a control method for battery energy storage system (BESS) that can improve the transient stability of power systems and therefore better accommodate the variability and the flexible operation of the future smart grid. The main results of this research are listed as follows: • Based on the linearized system model, the mechanism of dynamic interaction between CBGs and synchronous generators is revealed and the conditions for strong interactions are identified. • The transient stability mechanism of CBGs is analyzed and an index to quantify the transient stability margin of CBGs is proposed. The index can be obtained analytically without running dynamic simulations. • The impact of TTC on power system stability is investigated. Various forms of instability that can be triggered by TTC are identified and discussed. A simulation based method to assess system stability efficiently following a TTC action is proposed. • A novel wide area control method is proposed for BESSs to improve power system transient stability. The proposed control method has clear physical meanings and does not require system model or post-disturbance steady state information, which makes it suitable for future smart grid application with uncertainties and constantly changing operating conditions