Chen-Ching Liu

Bio: Chen-Ching Liu is an academic researcher from Virginia Tech. The author has contributed to research in topics: Electric power system & Electricity market. The author has an hindex of 57, co-authored 269 publications receiving 12126 citations. Previous affiliations of Chen-Ching Liu include Washington State University & Purdue University.

Loss minimization of distribution feeders: optimality and algorithms

Abstract: Determining open switch positions for the minimum-loss configuration of a radial distribution system is a discrete optimization problem. The authors present a global optimality condition of the problem and two algorithms: one is based on the uniformly distributed load model and the other on the concentrated current (or power) demand model. The derivation of the optimality condition relies on a concept of 'basic' current profiles of feeder sections and a transformation of the optimization problem. In the first algorithm, a closed form of the minimum-loss open point of a feeder pair can be found since the loss function is piecewise-parabolic. The algorithm obtains the optimal solution when the minimum is obtained for every feeder pair. In the second algorithm, a similar procedure is performed by moving open points, one at a time, from an actual switch position to another until no further loss reduction can be achieved. The proposed algorithm can be utilized as an online aid to distribution system operators. >

Coordinating Multiple Sources for Service Restoration to Enhance Resilience of Distribution Systems

Abstract: When a major outage occurs on a distribution system due to extreme events, microgrids, distributed generators, and other local resources can be used to restore critical loads and enhance resiliency. This paper proposes a decision-making method to determine the optimal restoration strategy coordinating multiple sources to serve critical loads after blackouts. The critical load restoration problem is solved by a two-stage method with the first stage deciding the post-restoration topology and the second stage determining the set of loads to be restored and the outputs of sources. In the second stage, the problem is formulated as a mixed-integer semidefinite program (SDP). The objective is maximizing the number of loads restored, weighted by their priority. The unbalanced three-phase power flow constraint and other operational constraints are considered. An iterative algorithm is proposed to deal with integer variables and can attain the global optimum of the critical load restoration problem by solving a few SDPs in most cases. The effectiveness of the proposed method is validated by numerical simulation with the modified IEEE 13-node test feeder and the modified IEEE 123-node test feeder under plenty of scenarios. The results indicate that the optimal restoration strategy can be determined efficiently in most scenarios.

Strategic Power Infrastructure Defense

Abstract: This paper provides a comprehensive state-of-the-art overview on power infrastructure defense systems. A review of the literature on the subjects of critical infrastructures, threats to the power grids, defense system concepts, and the special protection systems is reported. The proposed Strategic Power Infrastructure Defense (SPID) system methodology is a real-time, wide-area, adaptive protection and control system involving the power, communication, and computer infrastructures. The SPID system performs the failure analysis, vulnerability assessment, and adaptive control actions to avoid catastrophic power outages. This paper also includes a new concept for bargaining by multiagents to identify the decision options to reduce the system vulnerability. The concept of a flexible configuration of the wide-area grid is substantiated with an area-partitioning algorithm. A 179-bus system is used to illustrate the area partitioning method that is intended to minimize the total amount of load shedding.

Cybersecurity for distributed energy resources and smart inverters

Abstract: The increased penetration of distributed energy resources (DER) will significantly increase the number of devices that are owned and controlled by consumers and third-parties. These devices have a significant dependency on digital communication and control, which presents a growing risk from cyber-attacks. This study proposes a holistic attack-resilient framework to protect the integrated DER and the critical power grid infrastructure from malicious cyber-attacks, helping ensure the secure integration of DER without harming the grid reliability and stability. Specifically, the authors discuss the architecture of the cyber-physical power system with a high penetration of DER and analyse the unique cybersecurity challenges introduced by DER integration. Next, they summarise important attack scenarios against DER, propose a systematic DER resilience analysis methodology, and develop effective and quantifiable resilience metrics and design principles. Finally, they introduce attack prevention, detection, and response measures specifically designed for DER integration across cyber, physical device, and utility layers of the future smart grid.

Evaluating the Feasibility to Use Microgrids as a Resiliency Resource

Abstract: Regulated electricity utilities are required to provide safe and reliable service to their customers at a reasonable cost. To balance the objectives of reliable service and reasonable cost, utilities build and operate their systems to operate under typical historic conditions. As a result, when abnormal events such as major storms or disasters occur, it is not uncommon to have extensive interruptions in service to the end-use customers. Because it is not cost effective to make the existing electrical infrastructure 100% reliable, society has come to expect disruptions during abnormal events. However, with the increasing number of abnormal weather events, the public is becoming less tolerant of these disruptions. One possible solution is to deploy microgrids as part of a coordinated resiliency plan to minimize the interruption of power to essential loads. This paper evaluates the feasibility of using microgrids as a resiliency resource, including their possible benefits and the associated technical challenges. A use-case of an operational microgrid is included.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

The CMS experiment at the CERN LHC

Abstract: The Compact Muon Solenoid (CMS) detector is described. The detector operates at the Large Hadron Collider (LHC) at CERN. It was conceived to study proton-proton (and lead-lead) collisions at a centre-of-mass energy of 14 TeV (5.5 TeV nucleon-nucleon) and at luminosities up to 10(34)cm(-2)s(-1) (10(27)cm(-2)s(-1)). At the core of the CMS detector sits a high-magnetic-field and large-bore superconducting solenoid surrounding an all-silicon pixel and strip tracker, a lead-tungstate scintillating-crystals electromagnetic calorimeter, and a brass-scintillator sampling hadron calorimeter. The iron yoke of the flux-return is instrumented with four stations of muon detectors covering most of the 4 pi solid angle. Forward sampling calorimeters extend the pseudo-rapidity coverage to high values (vertical bar eta vertical bar <= 5) assuring very good hermeticity. The overall dimensions of the CMS detector are a length of 21.6 m, a diameter of 14.6 m and a total weight of 12500 t.

Smart Grid — The New and Improved Power Grid: A Survey

Abstract: The Smart Grid, regarded as the next generation power grid, uses two-way flows of electricity and information to create a widely distributed automated energy delivery network. In this article, we survey the literature till 2011 on the enabling technologies for the Smart Grid. We explore three major systems, namely the smart infrastructure system, the smart management system, and the smart protection system. We also propose possible future directions in each system. colorred{Specifically, for the smart infrastructure system, we explore the smart energy subsystem, the smart information subsystem, and the smart communication subsystem.} For the smart management system, we explore various management objectives, such as improving energy efficiency, profiling demand, maximizing utility, reducing cost, and controlling emission. We also explore various management methods to achieve these objectives. For the smart protection system, we explore various failure protection mechanisms which improve the reliability of the Smart Grid, and explore the security and privacy issues in the Smart Grid.