Carlos E. Murillo-Sánchez

Bio: Carlos E. Murillo-Sánchez is an academic researcher from National University of Colombia. The author has contributed to research in topics: Electric power system & Power system simulation. The author has an hindex of 14, co-authored 25 publications receiving 5751 citations. Previous affiliations of Carlos E. Murillo-Sánchez include National University of Colombia at Manizales & Cornell University.

Thermal unit commitment including optimal AC power flow constraints

Abstract: The authors propose a new algorithm for unit commitment that employs a Lagrange relaxation technique with a new augmentation of the Lagrangian. The new augmentation involves a duplication of variables that allows relaxation of the coupling between generator time-spanning constraints and system-wide instantaneous constraints. This framework allows the possibility of committing units that are required for the VArs that they can produce, as well as for their real power. Furthermore, although the algorithm is very CPU-intensive, the separation structure of the Lagrangian allows its implementation in parallel computers. Their work builds upon that of Batut and Renaud (1992), as well as that of Baldick (1995).

A stochastic, contingency-based security-constrained optimal power flow for the procurement of energy and distributed reserve

Abstract: It is widely agreed that optimal procurement of reserves, with explicit consideration of system contingencies, can improve reliability and economic efficiency in power systems. With increasing penetration of uncertain generation resources, this optimal allocation is becoming even more crucial. Herein, a problem formulation is developed to solve the day-ahead energy and reserve market allocation and pricing problem that explicitly considers the redispatch set required by the occurrence of contingencies and the corresponding optimal power flow, static and dynamic security constraints. Costs and benefits, including those arising from eventual demand deviation and contingency-originated redispatch and shedding, are weighted by the contingency probabilities, resulting in a scheme that contracts the optimal amount of resources in a stochastic day-ahead procurement setting. Furthermore, the usual assumption that the day-ahead contracted quantities correspond to some base case dispatch is removed, resulting in an optimal procurement as opposed to an optimal dispatch. Inherent in the formulation are mechanisms for rescheduling and pricing dispatch deviations arising from realized demand fluctuations and contingencies. Because the formulation involves a single, one stage, comprehensive mathematical program, the Lagrange multipliers obtained at the solution are consistent with shadow prices and can be used to clear the day-ahead and spot markets of the different commodities involved. Co-optimization of energy and reserves, including system contingency requirementsComplete AC power flow formulation with static and dynamic security constraintsLagrange multipliers determine various day-ahead and spot market commodity prices.Comparison with traditional method shows improvements in system security and costs.

Stochastically Optimized, Carbon-Reducing Dispatch of Storage, Generation, and Loads

Abstract: We present a new formulation of a hybrid stochastic-robust optimization and use it to calculate a look-ahead, security-constrained optimal power flow. It is designed to reduce carbon dioxide ( ${\rm CO}_{2}$ ) emissions by efficiently accommodating renewable energy sources and by realistically evaluating system changes that could reduce emissions. It takes into account ramping costs, ${\rm CO}_{2}$ damages, demand functions, reserve needs, contingencies, and the temporally linked probability distributions of stochastic variables such as wind generation. The inter-temporal trade-offs and transversality of energy storage systems are a focus of our formulation. We use it as part of a new method to comprehensively estimate the operational net benefits of system changes. Aside from the optimization formulation, our method has four other innovations. First, it statistically estimates the cost and ${\rm CO}_{2}$ impacts of each generator's electricity output and ramping decisions. Second, it produces a comprehensive measure of net operating benefit, and disaggregates that into the effects on consumers, producers, system operators, government, and ${\rm CO}_{2}$ damage. Third and fourth, our method includes creating a novel, modified Ward reduction of the grid and a thorough generator dataset from publicly available information sources. We then apply this method to estimating the impacts of wind power, energy storage, and operational policies.

An Internet-based platform for testing generation scheduling auctions

Abstract: The paper describes the uses and architecture of a network-centered computing-rich software platform called PowerWeb. PowerWeb was designed and built as a simulation environment for experimentally testing various power exchange auction markets through tournaments. It is designed to host simulations of a competitive "day-ahead" electric energy market in the content of a restructured electric power industry. The PowerWeb environment is meant to be flexible so as to accommodate different "rules of the game". The authors describe its interactive, distributed and Web-based character.

MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education

Abstract: MATPOWER is an open-source Matlab-based power system simulation package that provides a high-level set of power flow, optimal power flow (OPF), and other tools targeted toward researchers, educators, and students. The OPF architecture is designed to be extensible, making it easy to add user-defined variables, costs, and constraints to the standard OPF problem. This paper presents the details of the network modeling and problem formulations used by MATPOWER, including its extensible OPF architecture. This structure is used internally to implement several extensions to the standard OPF problem, including piece-wise linear cost functions, dispatchable loads, generator capability curves, and branch angle difference limits. Simulation results are presented for a number of test cases comparing the performance of several available OPF solvers and demonstrating MATPOWER's ability to solve large-scale AC and DC OPF problems.

Attack Detection and Identification in Cyber-Physical Systems

Abstract: Cyber-physical systems are ubiquitous in power systems, transportation networks, industrial control processes, and critical infrastructures. These systems need to operate reliably in the face of unforeseen failures and external malicious attacks. In this paper: (i) we propose a mathematical framework for cyber-physical systems, attacks, and monitors; (ii) we characterize fundamental monitoring limitations from system-theoretic and graph-theoretic perspectives; and (ii) we design centralized and distributed attack detection and identification monitors. Finally, we validate our findings through compelling examples.

Zero Duality Gap in Optimal Power Flow Problem

Abstract: The optimal power flow (OPF) problem is nonconvex and generally hard to solve. In this paper, we propose a semidefinite programming (SDP) optimization, which is the dual of an equivalent form of the OPF problem. A global optimum solution to the OPF problem can be retrieved from a solution of this convex dual problem whenever the duality gap is zero. A necessary and sufficient condition is provided in this paper to guarantee the existence of no duality gap for the OPF problem. This condition is satisfied by the standard IEEE benchmark systems with 14, 30, 57, 118, and 300 buses as well as several randomly generated systems. Since this condition is hard to study, a sufficient zero-duality-gap condition is also derived. This sufficient condition holds for IEEE systems after small resistance (10-5 per unit) is added to every transformer that originally assumes zero resistance. We investigate this sufficient condition and justify that it holds widely in practice. The main underlying reason for the successful convexification of the OPF problem can be traced back to the modeling of transformers and transmission lines as well as the non-negativity of physical quantities such as resistance and inductance.

Attack Detection and Identification in Cyber-Physical Systems -- Part I: Models and Fundamental Limitations

Abstract: Cyber-physical systems integrate computation, communication, and physical capabilities to interact with the physical world and humans. Besides failures of components, cyber-physical systems are prone to malignant attacks, and specific analysis tools as well as monitoring mechanisms need to be developed to enforce system security and reliability. This paper proposes a unified framework to analyze the resilience of cyber-physical systems against attacks cast by an omniscient adversary. We model cyber-physical systems as linear descriptor systems, and attacks as exogenous unknown inputs. Despite its simplicity, our model captures various real-world cyber-physical systems, and it includes and generalizes many prototypical attacks, including stealth, (dynamic) false-data injection and replay attacks. First, we characterize fundamental limitations of static, dynamic, and active monitors for attack detection and identification. Second, we provide constructive algebraic conditions to cast undetectable and unidentifiable attacks. Third, by using the system interconnection structure, we describe graph-theoretic conditions for the existence of undetectable and unidentifiable attacks. Finally, we validate our findings through some illustrative examples with different cyber-physical systems, such as a municipal water supply network and two electrical power grids.

Branch Flow Model: Relaxations and Convexification—Part II

Abstract: We propose a branch flow model for the analysis and optimization of mesh as well as radial networks. The model leads to a new approach to solving optimal power flow (OPF) that consists of two relaxation steps. The first step eliminates the voltage and current angles and the second step approximates the resulting problem by a conic program that can be solved efficiently. For radial networks, we prove that both relaxation steps are always exact, provided there are no upper bounds on loads. For mesh networks, the conic relaxation is always exact but the angle relaxation may not be exact, and we provide a simple way to determine if a relaxed solution is globally optimal. We propose convexification of mesh networks using phase shifters so that OPF for the convexified network can always be solved efficiently for an optimal solution. We prove that convexification requires phase shifters only outside a spanning tree of the network and their placement depends only on network topology, not on power flows, generation, loads, or operating constraints. Part I introduces our branch flow model, explains the two relaxation steps, and proves the conditions for exact relaxation. Part II describes convexification of mesh networks, and presents simulation results.