Phasor measurement units (PMUs) have become instrumental in modern power systems for enabling real time, wide-area monitoring and control. Accordingly, many studies have investigated efficient and ...robust dynamic state estimation (DSE) methods in order to accurately compute the dynamic states of generation units. Nonetheless, most of them forego the dynamic-algebraic nature of power networks and only consider their nonlinear dynamic representations. Motivated by the lack of DSE methods based on power network's differential-algebraic equations (DAEs), this article develops a novel observer-based DSE framework in order to perform simultaneous estimation of the dynamic and algebraic states of multimachine power networks. Specifically, we leverage the DAE dynamics of a power network around an operating point and combine them with a PMU-based measurement model capable of capturing bus voltages and line currents. The proposed <inline-formula><tex-math notation="LaTeX">\mathcal {H}_{\infty }</tex-math></inline-formula> observer, which only requires detectability and impulse observability conditions that are satisfied for various power networks, is designed to handle various noise, unknown inputs, and input sensor failures. The results obtained from performing extensive numerical simulations on IEEE 9-bus and 39-bus systems showcase the effectiveness of the proposed approach for DSE purposes.
Coupling Load-Following Control With OPF Bazrafshan, Mohammadhafez; Gatsis, Nikolaos; Taha, Ahmad F. ...
IEEE transactions on smart grid,
05/2019, Letnik:
10, Številka:
3
Journal Article
Recenzirano
Odprti dostop
In this paper, the optimal power flow (OPF) problem is augmented to account for the costs associated with the load-following control of a power network. Load-following control costs are expressed ...through the linear quadratic regulator (LQR). The power network is described by a set of nonlinear differential algebraic equations (DAEs). By linearizing the DAEs around a known equilibrium, a linearized OPF that accounts for steady-state operational constraints is formulated first. This linearized OPF is then augmented by a set of linear matrix inequalities that are algebraically equivalent to the implementation of an LQR controller. The resulting formulation, termed LQR-OPF, is a semidefinite program which furnishes optimal steady-state setpoints and an optimal feedback law to steer the system to the new steady state with minimum load-following control costs. Numerical tests demonstrate that the setpoints computed by LQR-OPF result in lower overall costs and frequency deviations compared to the setpoints of a scheme where OPF and load-following control are considered separately.
Addressing challenges in urban water infrastructure systems, including aging infrastructure, supply uncertainty, extreme events, and security threats, depends highly on water distribution networks ...modeling emphasizing the importance of realistic assumptions, modeling complexities, and scalable solutions. In this study, we propose a derivative‐free, linear approximation for solving the network water flow problem. The proposed approach takes advantage of the special form of the nonlinear head loss equations, and, after the transformation of variables and constraints, the water flow problem reduces to a linear optimization problem that can be efficiently solved by modern linear solvers. Ultimately, the proposed approach amounts to solving a series of linear optimization problems. We demonstrate the proposed approach through several case studies and show that the approach can model arbitrary network topologies and various types of valves and pumps, thus providing modeling flexibility. Under mild conditions, we show that the proposed linear approximation converges. We provide sensitivity analysis and discuss in detail the current limitations of our approach and suggest solutions to overcome these. All the codes, tested networks, and results are freely available on Github for research reproducibility.
Key Points
A new method for solving the water flow problem is proposed and tested
The method considers arbitrary network topology, flow direction, and various valve types
The approach is scalable to large water networks and can be seamlessly integrated for network actuator control and state estimation routines
We propose methods to solve time-varying, sensor and actuator (SaA) selection problems for uncertain cyber-physical systems. We show that many SaA selection problems for optimizing a variety of ...control and estimation metrics can be posed as semidefinite optimization problems with mixed-integer bilinear matrix inequalities (MIBMIs). Although this class of optimization problems is computationally challenging, we present tractable approaches that directly tackle MIBMIs, providing both upper and lower bounds, and that lead to effective heuristics for SaA selection. The upper and lower bounds are obtained via successive convex approximations and semidefinite programming relaxations, respectively, and selections are obtained with a slicing algorithm from the solutions of the bounding problems. Custom branch-and-bound and combinatorial greedy approaches are also developed for a broad class of systems for comparison. Finally, comprehensive numerical simulations are performed to compare the different methods and illustrate their effectiveness.
AbstractThe BATtle of the Attack Detection ALgorithms (BATADAL) is the most recent competition on planning and management of water networks undertaken within the Water Distribution Systems Analysis ...Symposium. The goal of the battle was to compare the performance of algorithms for the detection of cyber-physical attacks, whose frequency has increased in the last few years along with the adoption of smart water technologies. The design challenge was set for the C-Town network, a real-world, medium-sized water distribution system operated through programmable logic controllers and a supervisory control and data acquisition (SCADA) system. Participants were provided with data sets containing (simulated) SCADA observations, and challenged to design an attack detection algorithm. The effectiveness of all submitted algorithms was evaluated in terms of time-to-detection and classification accuracy. Seven teams participated in the battle and proposed a variety of successful approaches leveraging data analysis, model-based detection mechanisms, and rule checking. Results were presented at the Water Distribution Systems Analysis Symposium (World Environmental and Water Resources Congress) in Sacramento, California on May 21–25, 2017. This paper summarizes the BATADAL problem, proposed algorithms, results, and future research directions.
This paper is concerned with the Z-Bus method to solve the load-flow problem in three-phase distribution networks with wye and delta constant-power, constant-current, and constant-impedance loads ...(ZIP loads). The Z-Bus method is viewed as a fixed-point iteration. By leveraging the contraction mapping theorem, a set of sufficient conditions is then presented that guarantees a) the existence of a unique solution over a region that can be computed from the network parameters, and b) the convergence of the Z-Bus method to the unique solution. It is numerically illustrated that the new set of sufficient conditions holds for practical distribution networks and improves the previously reported results on the convergence of the Z-Bus method in three-phase networks.
This work proposes a novel sparsity-based decomposition method for the correlator output signals in GPS receivers capable of detecting spoofing attacks. We model complex correlator outputs of the ...received signal to form a dictionary of triangle-shaped replicas and employ a sparsity technique that selects potential matching triangle replicas from said dictionary. We formulate an optimization problem at the receiver correlator domain by using the Least Absolute Shrinkage and Selection Operator (LASSO) to find sparse code-phase peaks where such triangle-shaped delays are located. The optimal solution of this optimization technique discriminates two different code-phase values as authentic and spoofed peaks in a sparse vector output. We use a threshold to mitigate false alarms. Additionally, we present an expansion of the model by enhancing the dictionary to a collection of shifted triangles with higher resolution. Our experiments are able to discriminate authentic and spoofer peaks from synthetic GPS-like simulations. We also test our method on a real dataset, namely the Texas Spoofing Test Battery (TEXBAT). Our method achieves less than 1% detection error rate (DER) in nominal signal-to-noise ratio (SNR) conditions.
Optimal, network-driven control of water distribution networks (WDNs) is very difficult: valve and pump models form nontrivial, combinatorial logic; hydraulic models are nonconvex; water demand ...patterns are uncertain; and WDNs are naturally of large scale. Prior research on control of WDN addressed major research challenges, yet either i) adopted simplified hydraulic models, WDN topologies, and rudimentary valve/pump modeling or ii) used mixed-integer, nonconvex optimization to solve WDN control problems. The objective of this article is to develop tractable computational algorithms to manage WDN operation, while considering arbitrary topology, flow direction, an abundance of valve types, control objectives, hydraulic models, and operational constraints-all while only using convex, continuous optimization. Specifically, we propose new geometric programming (GP)-based model predictive control (MPC) algorithms, designed to solve the water flow equations and obtain WDN controls, i.e., pump/valve schedules alongside heads and flows. The proposed approach amounts to solving a series of convex optimization problems that graciously scale to large networks. The proposed approach is tested using a 126-node network with many valves and pumps and is shown to outperform traditional, rule-based control. The developed GP-based MPC algorithms, as well as the numerical test results, are all included on Github.
A risk-averse strategy is presented for optimal placement and sizing of photovoltaic (PV) inverters in distribution networks under solar irradiance and load uncertainty. By modeling uncertainties ...through a finite set of scenarios, a two-stage stochastic program is formulated. First-stage decisions comprise binary placement variables, area, and apparent power capacity of PV units. Second-stage decisions are PV reactive power compensations and power flows. The objective accounts for PV installation costs, expected thermal losses and a risk measure thereof. The formulation amounts to a mixed-integer second-order cone program and is tested on the IEEE 34-node test feeder.
Uncertainty from renewable energy and loads is one of the major challenges for stable grid operation. Various approaches have been explored to remedy these uncertainties. In this paper, we design ...centralized or decentralized state-feedback controllers for generators while considering worst case uncertainty. Specifically, this paper introduces the notion of <inline-formula><tex-math notation="LaTeX">\mathcal {L}_{\infty }</tex-math></inline-formula> robust control and stability for uncertain power networks. An uncertain and nonlinear differential algebraic equation model of the network is presented. The model includes unknown disturbances from renewables and loads. Given an operating point, the linearized state-space presentation is given. Then, the notion of <inline-formula><tex-math notation="LaTeX">\mathcal {L}_{\infty }</tex-math></inline-formula> robust control and stability is discussed, resulting in a nonconvex optimization routine that yields a state feedback gain mitigating the impact of disturbances. The developed routine includes explicit input-bound constraints on generators' inputs and a measure of the worst case disturbance. The feedback control architecture can be centralized, distributed, or decentralized. Algorithms based on successive convex approximations are then given to address the nonconvexity. Case studies are presented showcasing the performance of the <inline-formula><tex-math notation="LaTeX">\mathcal {L}_{\infty }</tex-math></inline-formula> controllers in comparison with automatic generation control and <inline-formula><tex-math notation="LaTeX">\mathcal {H}_{\infty }</tex-math></inline-formula> control methods.
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