Improving the Efficiency of Energy Harvesting Embedded System

In the past decade, mobile embedded systems, such as cell phones and tablets have infiltrated and dramatically transformed our life. The computation power, storage capacity and data communication speed of mobile devices have increases tremendously, and they have been used for more critical applications with intensive computation/communication. As a result, the battery lifetime becomes increasingly important and tends to be one of the key considerations for the consumers. Researches have been carried out to improve the efficiency of the lithium ion battery, which is a specific member in the more general Electrical Energy Storage (EES) family and is widely used in mobile systems, as well as the efficiency of other electrical energy storage systems such as supercapacitor, lead acid battery, and nickel–hydrogen battery etc. Previous studies show that hybrid electrical energy storage (HEES), which is a mixture of different EES technologies, gives the best performance. On the other hand, the Energy Harvesting (EH) technique has the potential to solve the problem once and for all by providing green and semi-permanent supply of energy to the embedded systems. However, the harvesting power must submit to the uncertainty of the environment and the variation of the weather. A stable and consistent power supply cannot always be guaranteed. The limited lifetime of the EES system and the unstableness of the EH system can be overcome by combining these two together to an energy harvesting embedded system and making them work cooperatively. In an energy harvesting embedded systems, if the harvested power is sufficient for the workload, extra power can be stored in the EES element; if the harvested power is short, the energy stored in the EES bank can be used to support the load demand. How much energy can be stored in the charging phase and how long the EES bank lifetime will be are affected by many factors including the efficiency of the energy harvesting module, the input/output voltage of the DC-DC converters, the status of the EES elements, and the characteristics of the workload. In this thesis, when the harvesting energy is abundant, our goal is to store as much surplus energy as possible in the EES bank under the variation of the harvesting power and the workload power. We investigate the impact of workload scheduling and Dynamic Voltage and Frequency Scaling (DVFS) of the embedded system on the energy efficiency of the EES bank in the charging phase. We propose a fast heuristic algorithm to minimize the energy overhead on the DC-DC converter while satisfying the timing constraints of the embedded workload and maximizing the energy stored in the HEES system. The proposed algorithm improves the efficiency of charging and discharging in an energy harvesting embedded system. On the other hand, when the harvesting rate is low, workload power consumption is supplied by the EES bank. In this case, we try to minimize the energy consumption on the embedded system to extend its EES bank life. In this thesis, we consider the scenario when workload has uncertainties and is running on a heterogeneous multi-core system. The workload variation is represented by the selection of conditional branches which activate or deactivate a set of instructions belonging to a task. We employ both task scheduling and DVFS techniques for energy optimization. Our scheduling algorithm considers the statistical information of the workload to minimize the mean power consumption of the application while satisfying a hard deadline constraint. The proposed DVFS algorithm has pseudo linear complexity and achieves comparable energy reduction as the solutions found by mathematical programming. Due to its capability of slack reclaiming, our DVFS technique is less sensitive to small change in hardware or workload and works more robustly than other techniques without slack reclaiming

Energy challenges for ICT

The energy consumption from the expanding use of information and communications technology (ICT) is unsustainable with present drivers, and it will impact heavily on the future climate change. However, ICT devices have the potential to contribute signi - cantly to the reduction of CO2 emission and enhance resource e ciency in other sectors, e.g., transportation (through intelligent transportation and advanced driver assistance systems and self-driving vehicles), heating (through smart building control), and manu- facturing (through digital automation based on smart autonomous sensors). To address the energy sustainability of ICT and capture the full potential of ICT in resource e - ciency, a multidisciplinary ICT-energy community needs to be brought together cover- ing devices, microarchitectures, ultra large-scale integration (ULSI), high-performance computing (HPC), energy harvesting, energy storage, system design, embedded sys- tems, e cient electronics, static analysis, and computation. In this chapter, we introduce challenges and opportunities in this emerging eld and a common framework to strive towards energy-sustainable ICT

Energy Harvesting and Energy Storage Systems

This book discuss the recent developments in energy harvesting and energy storage systems. Sustainable development systems are based on three pillars: economic development, environmental stewardship, and social equity. One of the guiding principles for finding the balance between these pillars is to limit the use of non-renewable energy sources

A review of optimal planning active distribution system:Models, methods, and future researches

Due to the widespread deployment of distributed energy resources (DERs) and the liberalization of electricity market, traditional distribution networks are undergoing a transition to active distribution systems (ADSs), and the traditional deterministic planning methods have become unsuitable under the high penetration of DERs. Aiming to develop appropriate models and methodologies for the planning of ADSs, the key features of ADS planning problem are analyzed from the different perspectives, such as the allocation of DGs and ESS, coupling of operation and planning, and high-level uncertainties. Based on these analyses, this comprehensive literature review summarizes the latest research and development associated with ADS planning. The planning models and methods proposed in these research works are analyzed and categorized from different perspectives including objectives, decision variables, constraint conditions, and solving algorithms. The key theoretical issues and challenges of ADS planning are extracted and discussed. Meanwhile, emphasis is also given to the suitable suggestions to deal with these abovementioned issues based on the available literature and comparisons between them. Finally, several important research prospects are recommended for further research in ADS planning field, such as planning with multiple micro-grids (MGs), collaborative planning between ADSs and information communication system (ICS), and planning from different perspectives of multi-stakeholders

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

Multi-objective optimal battery placement in distribution networks

Due to high penetration of renewable energy resources in today\u27s electricity generation, considerable voltage fluctuations are witnessed in power systems. As an attempt to solve this issue, in this study, multi-objective optimal placement and sizing of distribution-level battery storage system is performed using semidefinite programing. Placement of one or multiple battery system is studied under various objectives including the cost, voltage regulation, reactive power dispatch, renewable resource curtailment, and minimum network power losses. Power flow equations are solved in the form of semidefinite constraints and the rank constraint is ignored. Additionally, combination of these objectives to form a multi-objective problem and regularization of the number of battery sites are studied. Finally, simulation results are provided to analyze the proposed formulation --Abstract, page iii

Highly efficient maximum power point tracking control technique for PV system under dynamic operating conditions

publishedVersio

Operation and planning of distribution networks with integration of renewable distributed generators considering uncertainties: a review

YesDistributed generators (DGs) are a reliable solution to supply economic and reliable electricity to customers. It is the last stage in delivery of electric power which can be defined as an electric power source connected directly to the distribution network or on the customer site. It is necessary to allocate DGs optimally (size, placement and the type) to obtain commercial, technical, environmental and regulatory advantages of power systems. In this context, a comprehensive literature review of uncertainty modeling methods used for modeling uncertain parameters related to renewable DGs as well as methodologies used for the planning and operation of DGs integration into distribution network.This work was supported in part by the SITARA project funded by the British Council and the Department for Business, Innovation and Skills, UK and in part by the University of Bradford, UK under the CCIP grant 66052/000000

Application of Power Electronics Converters in Smart Grids and Renewable Energy Systems

This book focuses on the applications of Power Electronics Converters in smart grids and renewable energy systems. The topics covered include methods to CO2 emission control, schemes for electric vehicle charging, reliable renewable energy forecasting methods, and various power electronics converters. The converters include the quasi neutral point clamped inverter, MPPT algorithms, the bidirectional DC-DC converter, and the push–pull converter with a fuzzy logic controller