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.

Optimal Opt-In Residential Time-of-Use Contract Based on Principal-Agent Theory

Abstract: Installation of smart meters is increasing world-wide, opening the possibility to implement time-of-use (TOU) tariffs to moderate peak loads. This study is focused on the design of an optimal opt-in residential TOU tariff. Residential consumers are assumed to act in their private interests and maximize utility in response to tariffs. The regulated utility has a broader interest in maximizing societal welfare. However, the regulated utility cannot impose household behavior and cannot directly observe household type. Instead, the regulated utility must offer contract options including both a TOU tariff and current pricing to allow households to self-select the plan best suited to their interests. This paper proposes a simple flexible household utility function that can be calibrated with minimal data to describe diverse household behaviors and reveal household responses to different prices. An optimal pricing model is designed for the regulated utility, taking account of asymmetric information and potential household opportunistic behavior. Using economic constructs from principal-agent theory, the pricing model ensures participation among households most aligned with the regulated utility's desire. The pricing model can also be extended for competitive markets. A case study is performed to demonstrate the benefit the optional TOU tariff can realize.

A european supergrid: present state and future challenges

Abstract: The construction of the North Sea Super Grid is the major step towards meetingthe future demand for electric power transmission in northern Europe. This gridwill likely also extend onshore towards the load centres, and eventually form theEuropean Super Grid.Large-scale electric power generation at remote locations will lead tosignificant long distance power flows with a preferred flow direction. A methodto identify these unidirectional flows has been developed and applied on a casestudy, indicating the importance to consider unidirectional flows when designinga super grid.Voltage source converter based HVDC appears to be the best technicalsolution for the implementation of long distance transmission in such an offshoresuper grid. AC technology appears to be the most convenient choice for offshorenodes, but DC might also gain importance in this field, if reliable and affordableDC protection systems become available. A meshed DC grid offers significantadvantages towards a solution with many independent point-to-point HVDClinks, but also here protection is an unsolved issue that has to be overcome first.Reliability assessment of HVDC-based super grids is still very difficult,because operational experience with new technologies like the modularmultilevel converter is limited. This leads to a lack of data to calculate thefailure probabilities.A test system with a DC grid and the connected AC grids has been developedto serve as a common reference for a variety of DC grid studies.Unlike classical AC grids, DC grids will be dominated by power electronicsand the system behaviour will be determined to a large extent by the controllersof those power electronic systems. Large-scale implementation of powerelectronics with inappropriate control design has led to problems in AC systemsbefore. Photovoltaic generation systems in Germany are a good example forthis.A simplified AC frequency model has been developed to assess how powerelectronic systems influence the grid frequency. This model has been used to simulate how photovoltaic generation systems in Germany can endanger systemstability. A ‘grid-friendly’ charging controller for plug-in electric vehicles withbattery storage has been developed, and simulations have indicated that thiscontrol can contribute significantly to system stability.Even though AC and DC grids have some significant differences, some ofthe general concepts and lessons regarding balancing are true for both, andtomorrow’s DC grids can learn from today’s AC challenges.The balance in a DC grid should be defined as a current balance rather thanan active power balance (as it is used in AC grids), and the voltage can serve asa balance indicator, similar to AC frequency in AC grids. The control base forcontrolling the voltage should also be current instead of active power, leadingto linear system behaviour and a linear control task.HVDC converter control methods can be regarded as cases of droop controlwith one or more linear segments in the characteristic control curve. Withinone linear segment of the control curve, a HVDC converter can be representedby the Thevenin or Norton equivalent circuit.To unify a variety of proposed control concepts, Undead-band droop controlhas been proposed as a general piece-wise linear voltage control, which includesall other proposed methods as special implementations of undead-band droopcontrol. This concept could also be applied for other tasks than DC voltagecontrol like AC frequency control.

Optimal Generator Start-Up Strategy for Power System Restoration

Abstract: During system restoration, it is critical to utilize the available black-start (BS) units to provide cranking power to non-black-start (NBS) units in such a way that the overall system generation capability will be maximized. The corresponding optimization problem is combinatorial with complex practical constraints that can vary with time. This paper provides a new formulation of generator start-up sequencing as a mixed integer linear programming (MILP) problem. The linear formulation leads to an optimal solution to this important problem that clearly outperforms heuristic or enumerative techniques in quality of so- lutions or computational speed. The proposed generator start-up strategy is intended to provide an initial starting sequence of all BS or NBS units. The method can provide updates on the system MW generation capability as the restoration process progresses. The IEEE 39-Bus system, American Electric Power (AEP), and Entergy test cases are used for validation of the generation ca- pability optimization. Simulation results demonstrate that the proposed MILP-based generator start-up sequencing algorithm is highly efficient.

Reliability analysis of distribution systems considering service restoration

Abstract: Distribution system restoration (DSR) is intended to restore service to interrupted customers by a sequence of switching operations. An effective DSR strategy reduces interruption duration and consequently enhances reliability of distribution systems. In this paper, a systematic method is proposed for reliability analysis of smart distribution systems with DSR technique and remote control capability. The spanning tree search algorithm is used to generate optimal DSR schemes that restore the maximal amount of load with a minimal number of switching operations. Switching sequence is decided by a set of rules. Breadth-first search technique is used to determine the restoration time for customers. A 4-feeder 1069-node unbalanced test system with microgrids, developed by Pacific Northwest National Laboratory (PNNL), is simulated to demonstrate the effectiveness of the proposed method.

Implementation of reliability-centered maintenance for circuit breakers

Abstract: Power system reliability is highly related to the way maintenance tasks on system equipment are performed. Time-based maintenance (TBM) has been practiced as the usual maintenance scheduling strategy in electrical energy systems for many years. While this approach has the advantage of simple scheduling and high availability, it is not the most cost effective. The objective of this project is to develop a generic decision support tool for maintenance of power circuit breakers based on the concept of reliability-centered maintenance (RCM). Reliability-centered maintenance not only considers the actual condition of a piece of equipment but also the relative importance of the equipment to the whole system and is expected to be more cost effective than the traditional approaches.

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.