Preliminary Study of a Cryogen-Free Cryostat for a Low-Temperature Superconducting Magnet

International Journal of Computational Engineering ScienceVol. 05, No. 01, pp. 81-89 (2004) No AccessEVOLUTIONARY PROGRAMMING BASED ECONOMIC DISPATCH WITH LINE FLOW CONSTRAINTSP. SOMASUNDARAM, K. KUPPUSAMY, and R. P. KUMUDINI DEVIP. SOMASUNDARAMSchool of Electrical and Electronics Engineering, Anna University, Chennai, Tamil Nadu 600 025, India Search for more papers by this author , K. KUPPUSAMYSchool of Electrical and Electronics Engineering, Anna University, Chennai, Tamil Nadu 600 025, India Search for more papers by this author , and R. P. KUMUDINI DEVISchool of Electrical and Electronics Engineering, Anna University, Chennai, Tamil Nadu 600 025, India Search for more papers by this author https://doi.org/10.1142/S1465876304002277Cited by:1 PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail AbstractThis paper presents an efficient, simple and new method for solving Economic Dispatch (ED) problem with line flow constraints through the application of Evolutionary Programming (EP). The controllable system quantities in the base-case state are optimized to minimize some defined objective function subject to the base-case operating constraints. A 10-bus system is taken for investigation. The ED results obtained using EP are compared with those obtained using quadratic programming. The investigations reveal that the proposed algorithm is relatively simple, reliable, efficient and suitable for on-line applications.Keywords:Power System OptimizationEconomic DispatchEvolutionary ProgrammingLine Flow Constraints References B. H. Chowdhury and S. Rahman, IEEE Transaction on power systems 5(4), 1248 (1990). Crossref, Google Scholar Nanda, L. Hari and M. L. Kothari, IEE Proceedings-C 141(1), 1 (1994). Google ScholarJ. Nandaet al., IEE Proceedings-C 139(1), 39 (1992). Google ScholarJ. Y. Fan and L. Zhang, IEEE Transactions on Power Systems 13(2), 320 (1998). Crossref, Google ScholarP. K. Hota, R. Chakrabarti and P. K. Chattopadhyay, Institute of Engineers (India) Journal-EL 81, 21 (2000). Google ScholarJ. Nanda and R. Badri Narayanan, Electrical Power and Energy Systems 24, 723 (2002). Crossref, Google Scholar D. B. Fogel , Evoluationary Computation: Toward a New Philosophy of Machine Intelligence ( IEEE Press , USA , 1995 ) . Google ScholarL. L. Lai and J. T. Ma, IEEE Transactions on Power Systems 12(1), 198 (1997). Crossref, Google ScholarH. T. Yang, P. C. Yang and C. L. Huang, IEEE Transactions on Power Systems 11(1), 112 (1996). Crossref, Google ScholarK. P. Wong and J. Yuryevich, IEEE Transactions on Power Systems 13(2), 301 (1998). Google ScholarT. Jayabarathi, G. Sadasivam and V. Ramachandran, Electric Power Systems Research 52, 261 (1999). Crossref, Google ScholarT. Jayabarathi and G. Sadasivam, European Transaction on Electrical Power 10(3), 167 (2000). Crossref, Google ScholarP. Attaviriyanupapet al., IEEE Transactions on Power Systems 17(2), 411 (2002). Crossref, Google Scholar FiguresReferencesRelatedDetailsCited By 1FDR PSO-based Transient Stability Constrained Optimal Power Flow Solution for Deregulated Power IndustryM. Anitha, S. Subramanian and R. Gnanadass28 Aug 2007 | Electric Power Components and Systems, Vol. 35, No. 11 Recommended Vol. 05, No. 01 Metrics History KeywordsPower System OptimizationEconomic DispatchEvolutionary ProgrammingLine Flow ConstraintsPDF download

Nowadays, microgrids are gaining importance in electric power generation and distribution environments due to their flexibility, versatility, scalability and the possibility of supplying ancillary services when connected to the grid. They allow for the customization of electric supply for very different types of consumers. Therefore, a new control model for power and energy management based on homeostaticity of electric power systems (EPS) is presented, which has been already analyzed and approved by ENEL Chile in its developmental stage. ENEL, the largest electric utility in the country, is interested in incorporating smart microgrids in the electricity distribution market, as part of a worldwide policy. Such microgrids are to be installed in buildings serviced by ENEL. To demonstrate the model’s utility, a Simulink model of a real microgrid is used, which is comprised of PV generation, energy storage, an air conditioning (AC) equipment and thermal storage of the building upon which the microgrid is installed. The behavior of every element is simulated, including the dynamic thermal model of the building in order to optimize energy management and power supply versus consumption. The behavior of the whole system is analyzed under different environmental profiles and energy consumption patterns using the proposed homeostaticity system.

The electric power system's seasonality would mitigate the problem of the energy volatility crisis. Seasonal identification is important in energy usage analysis to make a better an organization's energy performance. Identification of the seasonal component in a time series data is essential since it defines a fluctuation within an interval of time. This seasonal component can be identified as a periodic phenomenon. Signal processing models can be used in analyzing periodic data in the representation of sinusoidal function. Seasonal variations are an important factor influencing the behavior of an electric power and energy load. This paper introduces the application of deterministic functions in the Ornstein\textendash Uhlenbeck process as a sinusoidal signal processing model in identifying seasonal components represented in periodic terms. We conducted extensive experiments to validate the model on three datasets in electric power and energy systems namely electricity load, household electric power consumption, and power consumption of the three source stations. Further, it can be obtained that the periodic continuous-time-inhomogeneous signal of Ornstein\textendash Uhlenbeck model can be used to identify the seasonal term of the very short to medium horizon forecast of the electric power and energy system. Seasonal changes in energy consumption can be used in energy management in the scope of energy efficiency connected to renewable energy plans.

It is very important for the development of electric power big data technology to use the electric power knowledge. A new electric power knowledge theory model is proposed here to solve the problem of normalized modeled electric power knowledge for the management and analysis of electric power big data. Current modeling techniques of electric power knowledge are viewed as inadequate because of the complexity and variety of the relationships among electric power system data. Ontology theory and semantic web technologies used in electric power systems and in many other industry domains provide a new kind of knowledge modeling method. Based on this, this paper proposes the structure, elements, basic calculations and multidimensional reasoning method of the new knowledge model. A modeling example of the regulations defined in electric power system operation standard is demonstrated. Different forms of the model and related technologies are also introduced, including electric power system standard modeling, multi-type data management, unstructured data searching, knowledge display and data analysis based on semantic expansion and reduction. Research shows that the new model developed here is powerful and can adapt to various knowledge expression requirements of electric power big data. With the development of electric power big data technology, it is expected that the knowledge model will be improved and will be used in more applications.

An electric power system (EPS) with high-power-delivery and low-system-mass is required for wide body all electric aircraft (AEA). Cables are an essential component of this future EPS. At the cruising height of a wide-body aircraft the pressure is about 16.8 kPa. At that pressure, heat transfer to the ambient air by convection is strongly limited, so the distribution of temperature field across the aircraft cables is expected to be different from atmospheric pressure. The temperature field across the cable depends on the velocity field of the ambient air, which depends on the pressure and temperature. Also, to obtain the electric field of DC cables in the electric aircraft power system, the conductivity of the insulation, a function of temperature and electric field, should be calculated. In this paper, a 5 kV DC cable is studied at 16.8 kPa pressure to compare the cable's electric field and temperature field at low pressure to the atmospheric pressure. In our earlier research, we suggested novel EPS topologies for wide body AEA, and 5 kV DC is the voltage level that was taken into consideration. Moreover, the maximum permissible flowing current of the cable is investigated at the pressure of 16.8 kPa regarding the thermal limits of the cable. It is shown that at low pressures the temperature of the cable is highly increased compared to atmospheric pressure, so permissible current is lower than rated current.

In order to address the need for planar magnetic components with reduced weight and volume that can operate at high frequencies and high power densities in high temperature environments, this paper describes the feasibility of monolithic magnetic technology for the development of reliable high performance electrical power components and systems for next generation aerospace and more electric aircraft power management and distribution equipment applications. Monolithic magnetic technology involves the integration of all the magnetic and electronic components of a power converter into a single monolithic nonmagnetic module. The feasibility of the technology is demonstrated by integrating all magnetic components of a 100 W, 1 MHz, DC-DC, two-transistor forward power converter into a 63.5 mm/spl times/63.5 mm/spl times/5.0 mm single monolithic nonmagnetic module using a single plasma-spray fabrication process.

<div class="htmlview paragraph">A lot of electric components have been installed in a vehicle today for comfort, safety and environment. This tendency is said to be continued in the future. Therefore, additional power supplies such as exhaust gas electricity generation system and thermal electricity generation system have been developed in the world to supply additional electricity as well as an enlargement of an alternator. However, if these new electricity supply systems are installed in a present electric power system that is controlled based on a voltage feedback, each supply system cannot be controlled effectively, because it is difficult to control output power of each system independently. An electric power based control system, <u><b>V</b></u>ehicle <u><b>E</b></u>lectric power <u><b>F</b></u>low management system (<b>VEF</b>), has been developed to avoid this problem.</div> <div class="htmlview paragraph">Sum of required electric power is calculated based on electric loads power and battery charging power. This required power is allocated to each power supply system. This assignment is performed according to an index, a running cost of electricity. This index is called <b>DENPI</b> (<u><b>D</b></u>esign of <u><b>E</b></u>lectric <u><b>N</b></u>etwork with <u><b>P</b></u>ower <u><b>I</b></u>ndex). For example, <b>DENPI</b> of the alternator changes according to an engine torque, a rotational speed and efficiency. Therefore, electric power system can supply electricity more effectively by selecting power system according to <b>DENP</b>. <b>VEF</b> automatically allocates each power supply system according to <b>DENPI</b> of each power supply system. As this function has a unique structure, it can be extensively applied to new electricity generation systems in various categories of vehicles.</div> <div class="htmlview paragraph"><b>VEF</b> has been evaluated with a test vehicle, and the results show that this structure is applicable to a real vehicle and the running cost of electricity is improved 30% compared to a conventional system.</div>

For the research on crucial technologies of range-extended electric vehicle, the first problem to be solved is parameter matching and efficiency optimization for range-extended electric vehicle power and transmission system. Parameter matching and optimization of range-extended electric vehicle power and transmission system are multi-objective optimization problem. Evaluation and analysis of multi-objective optimization problem should be mutually independent and balanced. With the aim of guaranteeing vehicle’s comprehensive performance, a parameter matching and optimization method for range-extended electric vehicle power and transmission system is proposed in this paper. First, the house of quality model of range-extended electric vehicle is established to determine weight coefficient of vehicle performance indicator based on market requirements instead of experience. Based on co-simulation control model which is established in Matlab-Simulink and AVL-Cruise, 40 groups of orthogonal tests are performed, and the sensitivity of characteristic parameters is analyzed to explore the coupling law among vehicle performance indicators, so as to clarify the entry point for parameter matching and optimization. The simulation results show that the characteristic parameters not only have a significant influence but also have a coupling effect on the vehicle performance indicators. The analysis of variance shows that there is a limitation in optimal level combination of various factors only by range. Then, particle swarm optimization algorithm is selected to optimize the parameters of range-extended electric vehicle power and transmission system based on sensitivity analysis results obtained above. The study reveals that it is more efficient and reasonable to match the range-extended electric vehicle power and transmission system with a smaller battery capacity and a “medium-sized” auxiliary power unit which can achieve adequate dynamic performance, lower purchase cost, longer driving range and less energy consumption. Finally, a comparative simulation between the range-based analysis and particle swarm optimization-based analysis is conducted, the simulation results indicate that the optimized design parameters solution can significantly improve the technical indicators of the vehicle.

This dissertation contributes to our understanding of how plug-in hybrid electric vehicles (PHEVs) and plug-in battery-only electric vehicles (EVs)—collectively termed plug-in electric vehicles (PEVs)—could be successfully integrated with the electric power system. The research addresses issues at a diverse range of levels pertaining to light-duty vehicles, which account for the majority of highway vehicle miles traveled, energy consumed by highway travel modes, and carbon dioxide emissions from on-road sources. Specifically, the following topics are investigated: (i) On-board power electronics topologies for bidirectional vehicle-to-grid and grid-to-vehicle power transfer; (ii) The estimation of the electric energy and power consumption by fleets of light-duty PEVs; (iii) An operating framework for the scheduling and dispatch of electric power by PEV aggregators; (iv) The pricing of electricity by PHEV aggregators and how it affects the decision-making process of a cost-conscious PHEV owner; (v) The impacts on distribution systems from PEVs under aggregator control; (vi) The modeling of light-duty PEVs for long-term energy and transportation planning at a national scale.

As an example of estimating the environmental impact of hydrogen energy, we have estimated the emission of CO/sub 2/ from the energy system using hydrogen. For the analyses, electric power and natural gas (city gas) systems are compared. For longer period of utilization the hydrogen system may result in the less emission, while new facility construction may be necessary. Estimation results on the energy transportation using hydrogen pipeline is also summarized, which indicates that for the longer distance the pipeline has the advantage over the electric power system from the view point of CO/sub 2/ emission and also the energy loss if adequate pipes and initial pressures are selected.

Due to the increasing penetration of gas-fired units and power to gas facilities, the electrical power system and natural gas system are more and more bi-directionally coupled. To tackle the challenges on the optimal scheduling operation of an integrated gas and power systems (IGPS), this article focuses on developing a novel approach to build a continuous spatial-temporal optimal operation schedule model. In the light of different time constants of the electrical power and natural gas systems, the continuous spatial-temporal optimal operation schedule model of IGPS is formulated in function space. Additionally, Bernstein polynomials are used to reformulate the continuous spatial-temporal optimization problem of IGPS to mixed-integer linear programming. In the study cases, the simulation results of a simple integrated system and a combined IEEE 39-bus power system and Belgian natural gas network demonstrate the accuracy and effectiveness of the proposed model.

The objective of this thesis is to design distributed software agents for reliable operation of integrated electric power systems of modern electric warships. The automatic reconfiguration of electric shipboard power systems is an important step toward improved fight-through and self-healing capabilities of naval warships. The improvements are conceptualized by redesigning the electric power system and its controls. This research focuses on a new scheme for an energy management system in the form of distributed control/software agents. Multiagent systems provide an ideal level of abstraction for modeling complex applications where distributed and heterogeneous entities need to cooperate to achieve a common goal. The agents' task is to ensure supply of the various load demands while taking into consideration system constraints and load and supply path priorities. A self-stabilizing maximum flow algorithm is investigated to allow implementation of the agents' strategies and find a global solution by only considering local information and a minimum amount of communication. (Abstract shortened by UMI.).

In recent years there has been an increase in the number of unmanned aerial vehicle (UAV) applications intended for various missions in a variety of environments. The adoption of the more‐electric aircraft has led to a greater emphasis on electrical power systems (EPS) for safe flight through an increased number of critical loads being sourced with electrical power. Despite extensive literature detailing the development of systems to detect UAV failures and enhance overall system reliability, few have focussed directly on the increasingly complex and dynamic EPS. This study outlines the development of a novel UAV EPS fault classification and diagnostic (FCD) system based on hidden Markov models (HMM) that will assist and improve EPS health management and control. The ability of the proposed FCD system to autonomously detect, classify and diagnose the severity of diverse EPS faults is validated with development of the system for NASA's advanced diagnostic and prognostic testbed (ADAPT), a representative UAV EPS system. EPS data from the ADAPT network was used to develop the FCD system and results described within this study show that a high classification and diagnostic accuracy can be achieved using the proposed system.

Demand and supply-side determinants of electric power consumption and representative roadmaps to 100% renewable systems

<div class="htmlview paragraph">Increasing electric loads in a vehicle causes over-discharge of a battery and drag torque due to an alternator. This paper gives a system concept of vehicle electric power flow management to solve these issues. Its primary function includes preserving electricity in a battery, stabilizing electric bus voltage, interfacing with vehicle torque control system, and improving fuel economy.</div> <div class="htmlview paragraph">The key point to realize such a system is a unified structure. It offers ‘Plug and Play’ function for electric power management components. Newly developed <u><b>V</b></u>ehicle <u><b>E</b></u>lectric Power <u><b>F</b></u>low Management System (<b>VEF</b> ) totally controls electric power flow in a vehicle. <b>VEF</b> contains an Electric Power Manager and its functional sub-systems, and controls them with the key parameter ‘electric power’. The sub-system includes Generation, Storage, Conversion, and Distribution to the loads. While sum of power required by electric loads exceeds a capacity of Generation and discharge of Storage, some electric loads are forced to reduce. These loads are selected according to the sum of required electric power and priority based on the load operating conditions. As <b>VEF</b> has a unified structure, it can be extensively applied for newcomer generators, batteries and loads in various categories of vehicles.</div> <div class="htmlview paragraph"><b>VEF</b> is evaluated with a test vehicle, and the results show that overall experiments are positive, including bus voltage stability and loads limitation with acceptable driver's feeling.</div>