Characteristics and applications of energy storage system to power network - A review

Maintaining reliability and stability of a power systems in transmission and distribution level becomes a big challenge in present scenario. Grid operators are always responsible to maintain equilibrium between available power generation and demand of end users. Maintaining grid balance is a bigger issue, in case of any unexpected generation shortage or grid disturbance or integration of any renewable energy sources like wind and solar power in the energy mix. In order to compensate such imbalance and to facilitate more renewable energy sources with the grid, energy storage system (ESS) started to be playing an important role with the advancement of the state of the art technology. ESS can also help to get reduction in greenhouse gas (GHG) emission by means of integrating more renewable energy sources to the grid. There are various types of Energy Storage (ES) technologies which are being used in power systems network from large scale (above 50MW) to small scale (up to 100KW). Based on the characteristics, each storage technology has their own merits and demerits. This paper carried out extensive review study and verifies merits and demerits of each storage technology and identifies the suitable technology for the future. This paper also has conducted feasibility study with the aid of E-SelectTM tool for various ES technologies in applications point of view at different grid locations. This review study helps to evaluate feasible ES technology for a particular electrical application and also helps to develop smart hybrid storage system for grid applications in efficient way.

Understanding the effects of electrical interference signals and the environment on the effectiveness of cathodic protection

Cathodic protection (CP) failure due to excursions from safe CP levels is a challenge for the protection and maintenance of buried energy pipelines. Although research shows that stray current is a major factor contributing to CP failure, there is little consensus on how 'big' the excursions (either in magnitude, length or frequency) need to be in order to cause pipeline corrosion problems. This uncertainty has caused difficulties in selecting suitable parameters in relevant industry standards. This paper provides a brief review of past research on different factors affecting CP efficiency. Preliminary results from new electrochemical cells designed to develop an understanding of how CP excursions away from the 'safe' level can lead to corrosion problems are also presented.

Defining and developing an energy retrofitting approach

This paper identifies the dilemma faced by the stakeholders of existing buildings in regards to a decision making process for energy retrofitting. This paper also identifies the missing stage viewed as the “integrity audit “which can lead to substantial savings in the area of building operation. The methodology is centered on identifying energy waste first, reducing the overall peak electrical demand and then retrofitting for energy-efficiency. A proposed “integrity audit” leads to the classification of three main energy culprits: the identification of waste, missed opportunities, and rescheduling the operation of equipment use. A case study indicating the financial advantages of applying this methodology for a commercial building are presented. The energy retrofitting strategy is divided into two main categories, namely building control improvements and building component implementation. The payback periods are often within months if not immediate.

Analysis of lightning current characteristics to investigate lightning strike damages to energy pipeline

Australia is one of the most lightning prone area on earth. Lightning strikes have been identified as one of the most common cause of energy pipeline damage in Australia. Therefore, a suitable protection schemes and mitigation strategies against lighting strike damage is very important for Australian pipeline industry. There are a number of research on lighting protection of establishment such as, power systems, buildings, and telecommunications systems, however, very few publications could be found which discuss about protection of pipeline from lightning strike. Assessment of effects in buried pipeline, due to lighting strikes is important. Existing models do not account adequately the effect of the characteristics of soil breakdown channels intercepted by the buried object. This paper aims to investigate the characteristics of lightning current on metal object under the soil of strike point so that lighting attachment to energy pipeline could be understand and a protection technique could be developed. Along with lightning current characteristics, lightning attachment process, distribution method, soil resistivity, propagation of lightning current in soil with a buried pipeline, pipeline electrical properties and other related areas and technologies is explored. The study shows that though there are some research on characteristics of induced on simple buried structures like narrow telephone cable or residential gas pipe, but no substantial research have been done on large comparatively complex structures like buried energy pipelines. Also dynamic behavior of soil and the object to be protected not been considered in protections schemes and experiments.

Uncertainty handling using neural network-based prediction intervals for electrical load forecasting

The complexity and level of uncertainty present in operation of power systems have significantly grown due to penetration of renewable resources. These complexities warrant the need for advanced methods for load forecasting and quantifying uncertainties associated with forecasts. The objective of this study is to develop a framework for probabilistic forecasting of electricity load demands. The proposed probabilistic framework allows the analyst to construct PIs (prediction intervals) for uncertainty quantification. A newly introduced method, called LUBE (lower upper bound estimation), is applied and extended to develop PIs using NN (neural network) models. The primary problem for construction of intervals is firstly formulated as a constrained single-objective problem. The sharpness of PIs is treated as the key objective and their calibration is considered as the constraint. PSO (particle swarm optimization) enhanced by the mutation operator is then used to optimally tune NN parameters subject to constraints set on the quality of PIs. Historical load datasets from Singapore, Ottawa (Canada) and Texas (USA) are used to examine performance of the proposed PSO-based LUBE method. According to obtained results, the proposed probabilistic forecasting method generates well-calibrated and informative PIs. Furthermore, comparative results demonstrate that the proposed PI construction method greatly outperforms three widely used benchmark methods. © 2014 Elsevier Ltd.

High-performance multifunctional graphene yarns: toward wearable all-carbon energy storage textiles

The successful commercialization of smart wearable garments is hindered by the lack of fully integrated carbon-based energy storage devices into smart wearables. Since electrodes are the active components that determine the performance of energy storage systems, it is important to rationally design and engineer hierarchical architectures atboth the nano- and macroscale that can enjoy all of the necessary requirements for a perfect electrode. Here we demonstrate a large-scale flexible fabrication of highly porous high-performance multifunctional graphene oxide (GO) and rGO fibers and yarns by taking advantage of the intrinsic soft self-assembly behavior of ultralarge graphene oxide liquid crystalline dispersions. The produced yarns, which are the only practical form of these architectures for real-life device applications, were found to be mechanically robust (Young's modulus in excess of 29 GPa) and exhibited high native electrical conductivity (2508 ± 632 S m(-1)) and exceptionally high specific surface area (2605 m(2) g(-1) before reduction and 2210 m(2) g(-1) after reduction). Furthermore, the highly porous nature of these architectures enabled us to translate the superior electrochemical properties of individual graphene sheets into practical everyday use devices with complex geometrical architectures. The as-prepared final architectures exhibited an open network structure with a continuous ion transport network, resulting in unrivaled charge storage capacity (409 F g(-1) at 1 A g(-1)) and rate capability (56 F g(-1) at 100 A g(-1)) while maintaining their strong flexible nature.

A computational framework for uncertainty integration in stochastic unit commitment with intermittent renewable energy sources

The penetration of intermittent renewable energy sources (IRESs) into power grids has increased in the last decade. Integration of wind farms and solar systems as the major IRESs have significantly boosted the level of uncertainty in operation of power systems. This paper proposes a comprehensive computational framework for quantification and integration of uncertainties in distributed power systems (DPSs) with IRESs. Different sources of uncertainties in DPSs such as electrical load, wind and solar power forecasts and generator outages are covered by the proposed framework. Load forecast uncertainty is assumed to follow a normal distribution. Wind and solar forecast are implemented by a list of prediction intervals (PIs) ranging from 5% to 95%. Their uncertainties are further represented as scenarios using a scenario generation method. Generator outage uncertainty is modeled as discrete scenarios. The integrated uncertainties are further incorporated into a stochastic security-constrained unit commitment (SCUC) problem and a heuristic genetic algorithm is utilized to solve this stochastic SCUC problem. To demonstrate the effectiveness of the proposed method, five deterministic and four stochastic case studies are implemented. Generation costs as well as different reserve strategies are discussed from the perspectives of system economics and reliability. Comparative results indicate that the planned generation costs and reserves are different from the realized ones. The stochastic models show better robustness than deterministic ones. Power systems run a higher level of risk during peak load hours.

Improving the toughness and electrical conductivity of epoxy nanocomposites by using aligned carbon nanofibres

There is an increasing demand for high performance composites with enhanced mechanical and electrical properties. Carbon nanofibres offer a promising solution but their effectiveness has been limited by difficulty in achieving directional alignment. Here we report the use of an alternating current (AC) electric field to align carbon nanofibres in an epoxy. During the cure process of an epoxy resin, carbon nanofibres (CNFs) are observed to rotate and align with the applied electric field, forming a chain-like structure. The fracture energies of the resultant epoxy nanocomposites containing different concentrations of CNFs (up to 1.6wt%) are measured using double cantilever beam specimens. The results show that the addition of 1.6wt% of aligned CNFs increases the electrical conductivity of such nanocomposites by about seven orders of magnitudes to 10^-2S/m and increases the fracture energy, G<inf>Ic</inf>, by about 1600% from 134 to 2345J/m². A modelling technique is presented to quantify this major increase in the fracture energy with aligned CNFs. The results of this research open up new opportunities to create multi-scale composites with greatly enhanced multifunctional properties.

Energy minimization in multi-task software-defined sensor networks

Abstract—
After a decade of extensive research on application-specific wireless sensor networks (WSNs), the recent development of information and communication technologies makes it practical to realize the software-defined sensor networks (SDSNs), which are able to adapt to various application requirements and to fully explore the resources of WSNs. A sensor node in SDSN is able to conduct multiple tasks with different sensing targets simultaneously. A given sensing task usually involves multiple sensors to achieve a certain quality-of-sensing, e.g., coverage ratio. It is significant to design an energy-efficient sensor scheduling and management strategy with guaranteed quality-of-sensing for all tasks. To this end, three issues are investigated in this paper: 1) the subset of sensor nodes that shall be activated, i.e., sensor activation, 2) the task that each sensor node shall be assigned, i.e., task mapping, and 3) the sampling rate on a sensor for a target, i.e., sensing scheduling. They are jointly considered and formulated as a mixed-integer with quadratic constraints programming (MIQP) problem, which is then reformulated into a mixed-integer linear programming (MILP) formulation with low computation complexity via linearization. To deal with dynamic events such as sensor node participation and departure, during SDSN operations, an efficient online algorithm using local optimization is developed. Simulation results show that our proposed online algorithm approaches the globally optimized network energy efficiency with much lower rescheduling time and control overhead.

Anodization parameters influencing the morphology and electrical properties of TiO2 nanotubes for living cell interfacing and investigations

Nanotube structures have attracted tremendous attention in recent years in many applications. Among such nanotube structures, titania nanotubes (TiO2) have received paramount attention in the medical domain due to their unique properties, represented by high corrosion resistance, good mechanical properties, high specific surface area, as well as great cell proliferation, adhesion and mineralization. Although lot of research has been reported in developing optimized titanium nanotube structures for different medical applications, however there is a lack of unified literature source that could provide information about the key parameters and experimental conditions required to develop such optimized structure. This paper addresses this gap, by focussing on the fabrication of TiO2 nanotubes through anodization process on both pure titanium and titanium alloys substrates to exploit the biocompatibility and electrical conductivity aspects, critical factors for many medical applications from implants to in-vivo and in-vitro living cell studies. It is shown that the morphology of TiO2 directly impacts the biocompatibility aspects of the titanium in terms of cell proliferation, adhesion and mineralization. Similarly, TiO2 nanotube wall thickness of 30-40nm has shown to exhibit improved electrical behaviour, a critical factor in brain mapping and behaviour investigations if such nanotubes are employed as micro-nano-electrodes.

Robot coordination for energy-balanced matching and sequence dispatch of robots to events

Given a set of events and a set of robots, the dispatch problem is to allocate one robot for each event to visit it. In a single round, each robot may be allowed to visit only one event (matching dispatch), or several events in a sequence (sequence dispatch). In a distributed setting, each event is discovered by a sensor and reported to a robot. Here, we present novel algorithms aimed at overcoming the shortcomings of several existing solutions. We propose pairwise distance based matching algorithm (PDM) to eliminate long edges by pairwise exchanges between matching pairs. Our sequence dispatch algorithm (SQD) iteratively finds the closest event-robot pair, includes the event in dispatch schedule of the selected robot and updates its position accordingly. When event-robot distances are multiplied by robot resistance (inverse of the remaining energy), the corresponding energy-balanced variants are obtained. We also present generalizations which handle multiple visits and timing constraints. Our localized algorithm MAD is based on information mesh infrastructure and local auctions within the robot network for obtaining the optimal dispatch schedule for each robot. The simulations conducted confirm the advantages of our algorithms over other existing solutions in terms of average robot-event distance and lifetime.

An efficient MAC protocol with adaptive energy harvesting for machine-to-machine networks

In a machine-to-machine network, the throughput performance plays a very important role. Recently, an attractive energy harvesting technology has shown great potential to the improvement of the network throughput, as it can provide consistent energy for wireless devices to transmit data. Motivated by that, an efficient energy harvesting-based medium access control (MAC) protocol is designed in this paper. In this protocol, different devices first harvest energy adaptively and then contend the transmission opportunities with energy level related priorities. Then, a new model is proposed to obtain the optimal throughput of the network, together with the corresponding hybrid differential evolution algorithm, where the involved variables are energy-harvesting time, contending time, and contending probability. Analytical and simulation results show that the network based on the proposed MAC protocol has greater throughput than that of the traditional methods. In addition, as expected, our scheme has less transmission delay, further enhancing its superiority.

Energy saving in electric heater of carbon fiber stabilization oven

Carbon fiber is an advanced material with high tensile strength and modulus, ideally suited for light weight applications. Carbon fiber properties are directly dependent on all aspects of production, especially the process step of thermal stabilization. Stabilization is considered to be one of the most critical process steps. Moreover, the stabilization process is the most energy consuming, time consuming and costly step. As oxidation is an exothermic process, constant airflow to uniformly remove heat from all tows across the towband is indispensable. Our approach is to develop an intelligent computational system that can construct an optimal Computational Fluid Dynamics (CFD) solution. In this study, an electrical heater has been designed by CFD modeling and intelligently controlled. The model results show that the uniform airflow and minimum turbulence kinetic energy can be achieved by combining intelligent system technology with CFD analysis strategy.

Performance evaluation of 3D printed miniature electromagnetic energy harvesters driven by air flow

As a renewable and non-polluting energy source, wind is used to produce electricity via large-diameter horizontal or vertical axis wind turbines. Such large wind turbines have been well designed and widely applied in industry. However, little attention has been paid to the design and development of miniature wind energy harvesters, which have great potential to be applied to the HVAC (heating, ventilating and air conditions) ventilation exhaust systems and household personal properties. In this work, 10 air-driven electromagnetic energy harvesters are fabricated using 3D printing technology. Parametric measurements are then conducted to study the effects of (1) the blade number, (2) its geometric size, (3) aspect ratio, presence or absence of (4) solid central shaft, (5) end plates, and (6) blade orientation. The maximum electrical power is 0.305 W. To demonstrate its practical application, the electricity generated is used to power 4 LED (light-emitting diode) lights. The maximum overall efficiency ηmax is approximately 6.59%. The cut-in and minimum operating Reynolds numbers are measured. The present study reveals that the 3D printed miniature energy harvesters provide a more efficient platform for harnessing ‘wind power’.

A Generalized hierarchical transactive energy management framework for residential microgrids

This work develops a transactive energy management system in order to automate the operation and efficiently utilize the energy generated from the solar PV unit and BESS in a single house as well as in the microgrid and provides cost-benefit analysis.