하이브리드 전력 저장 시스템의 설계 및 운용 최적화

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2013. 2. 장래혁.전기 에너지 저장 (electrical energy storage, EES) 시스템은 필요에 따라 에너지를 저장하였다가 사용함으로써 에너지 효율과 안정성을 높이고 에너지 단가를 낮추는 등의 기능을 한다. EES 시스템은 비상용 전기 공급, 부하 평준화, 첨두부하 분산, 재생에너지 발전을 위한 에너지 저장 등의 다양한 분야에서 응용할 수 있다. 현재 EES 시스템은 주로 단일 종류의 에너지 저장 기술을 사용하고 있는데, 아직까지 그 어떤 에너지 저장 기술도 높은 에너지 및 전력 밀도, 낮은 가격, 높은 충방전 효율, 긴 수명 등 이상적인 에너지 저장 기술의 모든 요건을 충족시키고 있지 못하고 있다. 하이브리드 전력 저장 (hybrid electrical energy storage, HEES) 시스템은 여러 다른 종류의 에너지 저장 소자를 이용하여 각각의 장점을 활용하여 단점을 보완하는 기법으로, EES 시스템의 성능을 개선시시키기 위한 실용적인 접근 방법 가운데 하나이다. HEES 시스템은 정교한 시스템 설계와 제어기법을 통해 각각의 에너지 저장 소자의 장점을 모두 합친 것과 같은 성능을 갖출 수 있다. 본 학위 논문은 HEES 시스템의 에너지 효율을 최대화하기 위한 고수준의 최적화 기법들을 소개한다. HEES 시스템의 새로운 구조들과 체계적인 최적 설계 기법들을 제시한다. 제안된 네트워크 전하 전송망 (charge transfer interconnect, CTI) 구조와 뱅크 (bank) 재구성 구조는 전력 변환 손실을 최소화하여 HEES 시스템의 전하 전송 효율을 최대화한다. 또한 기존의 제어 기법들이 가진 한계점을 지적하고, 이를 보완하기 위해 전력원을 동시에 고려하여 설계하고 제어하는 기법을 제시한다. 제안된 최대 전력 전달 추종 (maximum power transfer tracking, MPTT) 기법과 이를 고려한 설계 기법은 실직적인 에너지 수집량을 증가시키고 실제적으로 사용 가능한 에너지량을 증가시킨다. 마지막으로 제안된 기법의 실현 가능성을 검증하기 위한 HEES 시스템 프로토타입 구현을 소개한다.Electrical energy storage (EES) systems provides various benefits of high energy efficiency, high reliability, low cost, and so on, by storing and retrieving energy on demand. The applications of the EES systems are wide, covering contingency service, load leveling, peak shaving, energy buffer for renewable power sources, and so on. Current EES systems mainly rely on a single type of energy storage technology, but no single type of EES element can fulfill all the desirable characteristics of an ideal electrical energy storage, such as high power/energy density, low cost, high cycle efficiency, and long cycle life. A hybrid electrical energy storage (HEES) system is composed of multiple, heterogeneous energy storage elements, aiming at exploiting the strengths of each energy storage element while hiding its weaknesses, which is a practical approach to improve the performance of EES systems. A HEES system may achieve the a combination of performance metrics that are superior to those for any of its individual energy storage elements with elaborated system design and control schemes. This dissertation proposes high-level optimization approaches for HEES systems in order to maximize their energy efficiency. We propose new architectures for the HEES systems and systematic design optimization methods. The proposed networked charge transfer interconnect (CTI) architecture and bank reconfiguration architecture minimizes the power conversion loss and thus maximizes the charge transfer efficiency of the HEES system. We also point out the limitation of the conventional control schemes and propose a joint optimization design and control considering the power sources. The proposed maximum power transfer tracking (MPTT) operation and MPTT-aware design method effectively increases energy harvesting efficiency and actual available energy. We finally introduce a prototype of a HEES system implementation that physically proves the feasibility of the proposed HEES system.1 Introduction 1.1 Motivations 1.2 Contribution and Significance 1.3 Organization of Dissertation 2 Background and Related Work 2.1 Electrical Energy Storage Elements 2.1.1 Performance Metrics 2.1.1.1 Power and Energy Density 2.1.1.2 Capital Cost 2.1.1.3 Cycle Efficiency 2.1.1.4 State-of-Health and Cycle Life 2.1.1.5 Self-Discharge Rate 2.1.1.6 Environmental Impacts 2.1.2 Energy Storage Elements 2.1.2.1 Lead-Acid Batteries 2.1.2.2 Lithium-Ion Batteries 2.1.2.3 Nickel-Metal Hydride Batteries 2.1.2.4 Supercapacitors 2.1.2.5 Other Energy Storage Elements 2.2 Homogeneous Electrical Energy Storage Systems 2.2.1 Energy Storage Systems 2.2.2 Applications of EES Systems 2.2.2.1 Grid Power Generation 2.2.2.2 Renewable Energy 2.2.3 Previous Homogeneous EES Systems 2.2.3.1 Battery EES Systems 2.2.3.2 Supercapacitor EES Systems 2.2.3.3 Other EES Systems 2.3 Hybrid Electrical Energy Storage Systems 2.3.1 Hybridization Architectures 2.3.2 Applications of HEES Systems 2.4 EES System Components Characteristics 2.4.1 Power Converter 2.4.2 Photovoltaic Cell 3 Hybrid Electrical Energy Storage Systems 3.1 Design Considerations of HEES Systems 3.2 HEES System Architecture 3.3 Charge Transfer and Charge Management 3.4 HEES System Components 3.4.1 Nodes 3.4.1.1 Energy Storage Banks 3.4.1.2 Power Sources and Load Devices 3.4.2 Charge Transfer Interconnect 3.4.3 System Control and Communication Network 4 System Level Design Optimization 4.1 Reconfigurable Storage Element Array 4.1.1 Cycle Efficiency and Capacity Utilization of EES Bank 4.1.2 General Bank Reconfiguration Architecture 4.1.3 Dynamic Reconfiguration Algorithm 4.1.3.1 Cycle Efficiency 4.1.3.2 Capacity Utilization 4.1.4 Cycle Efficiency and Capacity Utilization Improvement 4.2 Networked Charge Transfer Interconnect 4.2.1 Networked Charge Transfer Interconnect Architecture 4.2.1.1 Charge Transfer Conflicts 4.2.1.2 Networked CTI Architecture 4.2.2 Conventional Placement and Routing Problems 4.2.3 Placement and Routing Problems 4.2.4 Force-Directed Node Placement 4.2.5 Networked Charge Transfer Interconnect Routing 4.2.6 Energy Efficiency Improvement 4.2.6.1 Experimental Setup 4.2.6.2 Experimental Results 5 Joint Optimization with Power Sources 5.1 Maximum Power Transfer Tracking 5.1.1 Maximum Power Transfer Point 5.1.1.1 Sub-Optimality of Maximum Power Point Tracking 5.1.1.2 Maximum Power Transfer Tracking 5.1.2 MPTT-Aware Energy Harvesting System Design 5.1.2.1 Optimal System Design Problem 5.1.2.2 Design Optimization 5.1.2.3 Systematic Design Optimization 5.1.2.4 Energy Harvesting Improvement 5.2 Photovoltaic Emulation for MPTT 5.2.1 Model Parameter Extraction 5.2.2 Dual-Mode Power Regulator with Power Hybridization 5.2.2.1 PV Module I-V Characteristics 5.2.2.2 Modes of Operation 5.2.2.3 Circuit Design Principle 5.2.2.4 Dual-Mode Power Regulator Control 5.2.2.5 Implementation 5.2.2.6 Experiments 6 Experiments 6.1 HEV Application 6.1.1 Regenerative Brake 6.1.2 PV Modules 6.1.3 EES Bank Reconfiguration and Networked CTI 6.1.4 Overall Improvement and Cost Analysis 6.2 HEES Prototype Implementation 6.2.1 Design Specifications 6.2.1.1 Power Input and Output 6.2.1.2 Power and Energy Capacity 6.2.1.3 Voltage and Current Ratings 6.2.1.4 EES Elements 6.2.2 Implementation 6.2.2.1 Bank Module 6.2.2.2 Controller and Converter Module 6.2.2.3 Charge Transfer Interconnect Capacitor Module 6.2.2.4 Bidirectional Charger 6.2.2.5 Supervising Control Software 6.2.2.6 Component Assembly 6.2.3 Control Method 7 Conclusions and Future DirectionsDocto

Design Space Exploration in Cyber-Physical Systems

Cyber physical systems (CPS) integrate a variety of engineering areas such as control, mechanical and computer engineering in a holistic design effort. While interdependencies between the different disciplines are key attributes of CPS design science, little is known about the impact of design decisions of the cyber part on the overall system qualities. To investigate these interdependencies, this paper proposes a simulation-based Design Space Exploration (DSE) framework that considers detailed cyber system parameters such as cache size, bus width, and voltage levels in addition to physical and control parameters of the CPS. We propose an exploration algorithm that surfs the parameter configurations in the cyber physical sub-systems, in order to approximate the Pareto-optimal design points with regards to the trade-os among the design objectives, such as energy consumption and control stability. We apply the proposed framework to a network control system for an inverted-pendulum application. The presented holistic evaluation of the identified Pareto-points reveals the presence of non-trivial trade-os, which are imposed by the control, physical, and detailed cyber parameters. For instance the identified energy and control optimal design points comprise configurations with a wide range of CPU speeds, sample times and cache configuration following non-trivial zig-zag patterns. The proposed framework could identify and manage those trade-os and, as a result, is an imperative rst step to automate the search for superior CSP configurations

FABRIC: A National-Scale Programmable Experimental Network Infrastructure

FABRIC is a unique national research infrastructure to enable cutting-edge and exploratory research at-scale in networking, cybersecurity, distributed computing and storage systems, machine learning, and science applications. It is an everywhere-programmable nationwide instrument comprised of novel extensible network elements equipped with large amounts of compute and storage, interconnected by high speed, dedicated optical links. It will connect a number of specialized testbeds for cloud research (NSF Cloud testbeds CloudLab and Chameleon), for research beyond 5G technologies (Platforms for Advanced Wireless Research or PAWR), as well as production high-performance computing facilities and science instruments to create a rich fabric for a wide variety of experimental activities

Optimal Economic Schedule for a Network of Microgrids With Hybrid Energy Storage System Using Distributed Model Predictive Control

Artículo Open Access en el sitio web el editor. Pago por publicar en abierto.In this paper, an optimal procedure for the economic schedule of a network of interconnected microgrids with hybrid energy storage system is carried out through a control algorithm based on distributed model predictive control (DMPC). The algorithm is specifically designed according to the criterion of improving the cost function of each microgrid acting as a single system through the network mode operation. The algorithm allows maximum economical benefit of the microgrids, minimizing the degradation causes of each storage system, and fulfilling the different system constraints. In order to capture both continuous/discrete dynamics and switching between different operating conditions, the plant is modeled with the framework of mixed logic dynamic. The DMPC problem is solved with the use of mixed integer linear programming using a piecewise formulation, in order to linearize a mixed integer quadratic programming problem.Ministerio de Economía, Industria y Competitivadad DPI2016-78338-RComisión Europea 0076-AGERAR-6-

Data Acquisition and Control System of Hydroelectric Power Plant Using Internet Techniques

Vodní energie se nyní stala nejlepším zdrojem elektrické energie na zemi. Vyrábí se pomocí energie poskytované pohybem nebo pádem vody. Historie dokazuje, že náklady na tuto elektrickou energii zůstávají konstantní v průběhu celého roku. Vzhledem k mnoha výhodám, většina zemí nyní využívá vodní energie jako hlavní zdroj pro výrobu elektrické energie.Nejdůležitější výhodou je, že vodní energie je zelená energie, což znamená, že žádné vzdušné nebo vodní znečišťující látky nejsou vyráběny, také žádné skleníkové plyny jako oxid uhličitý nejsou vyráběny, což činí tento zdroj energie šetrný k životnímu prostředí. A tak brání nebezpečí globálního oteplování. Použití internetové techniky k ovladání několika vodních elektráren má velmi významné výhody, jako snížení provozních nákladů a flexibilitu uspokojení změny poptávky po energii na straně spotřeby. Také velmi efektivně čelí velkým narušením elektrické sítě, jako je například přidání nebo odebrání velké zátěže, a poruch. Na druhou stranu, systém získávání dat poskytuje velmi užitečné informace pro typické i vědecké analýzy, jako jsou ekonomické náklady, predikce poruchy systémů, predikce poptávky, plány údržby, systémů pro podporu rozhodování a mnoho dalších výhod. Tato práce popisuje všeobecný model, který může být použit k simulaci pro sběr dat a kontrolní systémy pro vodní elektrárny v prostředí Matlab / Simulink a TrueTime Simulink knihovnu. Uvažovaná elektrárna sestává z vodní turbíny připojené k synchronnímu generátoru s budicí soustavou, generátor je připojen k veřejné elektrické síti. Simulací vodní turbíny a synchronního generátoru lze provést pomocí různých simulačních nástrojů. V této práci je upřednostňován SIMULINK / MATLAB před jinými nástroji k modelování dynamik vodní turbíny a synchronního stroje. Program s prostředím MATLAB SIMULINK využívá k řešení schematický model vodní elektrárny sestavený ze základních funkčních bloků. Tento přístup je pedagogicky lepší než komplikované kódy jiných softwarových programů. Knihovna programu Simulink obsahuje funkční bloky, které mohou být spojovány, upravovány a modelovány. K vytvoření a simulování internetových a Real Time systémů je možné použít bud‘ knihovnu simulinku Real-Time nebo TRUETIME, v práci byla použita knihovna TRUETIME.Hydropower has now become the best source of electricity on earth. It is produced due to the energy provided by moving or falling water. History proves that the cost of this electricity remains constant over the year. Because of the many advantages, most of the countries now have hydropower as the source of major electricity producer. The most important advantage of hydropower is that it is green energy, which mean that no air or water pollutants are produced, also no greenhouse gases like carbon dioxide are produced which makes this source of energy environment-friendly. It prevents us from the danger of global warming. Using internet techniques to control several hydroelectric plants has very important advantages, as reducing operating costs and the flexibility of meeting changes of energy demand occurred in consumption side. Also it is very effective to confront large disturbances of electrical grid, such as adding or removing large loads, and faults. In the other hand, data acquisition systems provides very useful information for both typical and scientific analysis, such as economical costs reducing, fault prediction systems, demand prediction, maintenance schedules, decision support systems and many other benefits. This thesis describes a generalized model which can be used to simulate a data acquisition and control system of hydroelectric power plant using MATLAB/SIMULINK and TrueTime simulink library. The plant considered consists of hydro turbine connected to synchronous generator with excitation system, and the generator is connected to public grid. Simulation of hydro turbine and synchronous generator can be done using various simulation tools, In this work, SIMULINK/MATLAB is favored over other tools in modeling the dynamics of a hydro turbine and synchronous machine. The SIMULINK program in MATLAB is used to obtain a schematic model of the hydro plant by means of basic function blocks. This approach is pedagogically better than using a compilation of program code as in other software programs .The library of SIMULINK software programs includes function blocks which can be linked and edited to model. Either Simulink Real-Time library or TrueTime library can be used to build and simulate internet and real time systems, in this thesis the TrueTime library was used.

UC Berkeley's Cory Hall: Evaluation of Challenges and Potential Applications of Building-to-Grid Implementation

From September 2009 through June 2010, a team of researchers developed, installed, and tested instrumentation on the energy flows in Cory Hall on the UC Berkeley campus to create a Building-to-Grid testbed. The UC Berkeley team was headed by Professor David Culler, and assisted by members from EnerNex, Lawrence Berkeley National Laboratory, California State University Sacramento, and the California Institute for Energy & Environment. While the Berkeley team mapped the load tree of the building, EnerNex researched types of meters, submeters, monitors, and sensors to be used (Task 1). Next the UC Berkeley team analyzed building needs and designed the network of metering components and data storage/visualization software (Task 2). After meeting with vendors in January, the UCB team procured and installed the components starting in late March (Task 3). Next, the UCB team tested and demonstrated the system (Task 4). Meanwhile, the CSUS team documented the methodology and steps necessary to implement a testbed (Task 5) and Harold Galicer developed a roadmap for the CSUS Smart Grid Center with results from the testbed (Task 5a) and evaluated the Cory Hall implementation process (Task 5b). The CSUS team also worked with local utilities to develop an approach to the energy information communication link between buildings and the utility (Task 6). The UC Berkeley team then prepared a roadmap to outline necessary technology development for Building-to-Grid, and presented the results of the project in early July (Task 7). Finally, CIEE evaluated the implementation, noting challenges and potential applications of Building-to-Grid (Task 8). These deliverables are available at the i4Energy site: http://i4energy.org/