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.

TWENTIES : conclusions of a major R&D and demonstration project on offshore DC grids

Abstract: European governments’ targets for renewable energy by 2020 will lead to large offshore wind power integration in the existing Power System. High-Voltage Direct Current (HVDC) provides the most suitable technology to enable massive integration of offshore wind farms into AC onshore grids over long distances, with great control on transmitted power. More specifically, DC Grids (DCG) based on Voltage Source Converters (VSC) are being widely investigated to integrate multiple offshore wind farms dispersed over wide areas into AC onshore networks. For three and a half years, the « DC GRID » demo within the TWENTIES European project was focussed on a wide range of challenging issues related to the DC grid benefits for connecting offshore intermittent power: offshore DCGs economic assessment and likely layouts; DCG control and protection; ancillary services provided by such grids to the mainland AC network. This paper presents major achievements of the TWENTIES project in these domains. In addition, two major outcomes of the « DC GRID » demo are physical demonstrators. One of them is a low scale DCG mock-up, on which some of the above controls, as well as DC grid protection algorithms were successfully tested. Last, a highly innovative DC Circuit Breaker (DCCB) prototype was developed to overcome a strong technological barrier: this new equipment was successfully tested in presence of an independent expert to establish the qualification of this technology in the HV domain.

Evaluation of Market Rules Using

Abstract: The California energy crisis in 2000-2001 showed what could happen to an electricity market if it did not go through a comprehensive and rigorous testing before its implementation. Due to the complexity of the market structure, strategic interaction between the participants, and the underlying physics, it is difficult to fully evaluate the implications of potential changes to market rules. This paper presents a flexible and integrative method to assess market designs through agent-based modeling. Realistic simulation scenarios are constructed for evaluation of the pro- posed PJM-like market power mitigation rules of the California electricity market. Simulation results show that in the absence of market power mitigation, generation company (GENCO) agents facilitated by Q-learning are able to exploit the market flaws and make significantly higher profits relative to the competitive benchmark. The incorporation of PJM-like local market power mitigation rules is shown to be effective in suppressing the exercise of market power.

An extended expert system for service restoration of distribution feeders

Abstract: The service restoration planning in distribution systems deals with the determination of necessary switching actions to restore the service of customers affected by an outage. In practice, it has been performed by system dispatchers based on experience. In this paper, an expert system which has the following capabilities is presented: two-phase service restoration considering zone priority and supervised switches, maintenance switching and user-specified zone priority ordering. The proposed expert system ERP (Enhanced Restoration Planner) is implemented in PROLOG and is able to come up with appropriate switching actions within a few seconds. The proposed expert system can be a powerful tool in aiding the distribution system dispatchers in emergency situations.

Risk calculation of an economic transmission project using Kolmogorov equations

Abstract: Merchant transmission projects are market based projects for importing cheap power from inexpensive power suppliers. Due to uncertainties in the energy market, market based cost recovery mechanisms have not been successful in guaranteeing full recovery of the investment cost. As a result, a regulated recovery via approved transmission rate is likely to be needed. In a previous paper, a method to apply perpetual option theory was proposed to allow an investor to decide when best to invest based on a cost recovery via regulated rate. The proposed method views the investment decision (e.g., construction of a new transmission line) as an option that can be exercised before the optimal condition at which the expected return is maximized under uncertainties. This paper is an extension of that paper. The probability of not recovering the capital investment associated with an early exercise of the option to build before the optimal condition is reached is calculated using Kolmogorov forward equation. The risk of an early exercise is needed for investment decision and for assessing the incentive needed to make the much needed transmission investment.

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.