• Energy Research
• 7. Clean energy close
• US close
• IN close
• AU close

Abstract The concept of using electrochemical gas concentration cells to convert the mechanical potential energy of ocean waves to electricity using a taut-moored buoy is analyzed. Several idealized embodiments are discussed and one of these is shown to have particular merit. Some results obtained in an experimental program aimed at developing such a system are described. In particular, an electrochemical cell employing the protonically conducting synthetic polymer Nafion, bounded by platinum electrodes, has been studied in a manner which simulates the operation of such a device within a taut-moored buoy subject to ocean waves. It is shown that with some modest engineering advances, this system is indeed capable converting a significant fraction of ocean wave energy into electricity.

The exploration of the Earth's oceans is aided by autonomous underwater vehicles (AUVs). AUVs in use today include floats and gliders; they can be deployed to profile salinity, temperature and pressure of the ocean at depths of up to 2 km. Both the floats and gliders typically control buoyancy by filling and deflating an external bladder with a hydraulic fluid delivered by an electrical pump. The operation time of an AUV is limited by energy storage. For floats, such as the Argo float, the operating duration is approximately 5 years with the capability to dive once every 10 days. For electric gliders, such as the deep G2 Slocum, the mission duration can be up to one year with lithium primary batteries. An energy storage system has been developed that can harvest energy from the temperature differences at various depths of the ocean. This system was demonstrated on an Argo style float and has been implemented in a thermal version of the Slocum glider. The energy harvesting system is based on a phase change material with a freeze thaw cycle that pressurizes hydraulic oil that is converted to electrical energy. The thermal Slocum glider does not use an electrical pump, but harvested thermal energy to control buoyancy. The goal for the thermal Slocum glider is for persistent ocean operation for a duration of up to 10 years. A thermal powered glider with an energy harvesting system as described can collect conductivity, temperature, and pressure data and deliver it to the National Data Buoy Center (NDBC) Glider Data Monitoring System and the World Meteorological Organization (WMO) Global Telecommunications System (GTS). Feeding into operational modeling centers such as the National Centers for Environmental Prediction (NCEP) and the U.S. Naval Observatory (NAVO), this data will enable advanced climate predictions over a timespan not currently achievable with present technology. Current testing of the thermal powered Slocum glider is to determine the durability of the technology and quantify the glider system design. Previous issues with this technology included energy storage system management and glider mechanical limitations. Our objective is to learn how to fly an energy harvesting thermal glider that interacts with the ocean environment efficiently. We would also like to establish the latitudinal range of operation. This thermal powered Slocum glider, dubbed Clark, after the famous explorer duo Lewis and Clark, has been deployed off of St. Thomas for flight dynamics and durability testing. The following paper will discuss the deployment and testing of the thermal powered Slocum glider. We will also discuss the advantages of ocean energy harvesting technology for oceanographic research.

Abstract As states and utilities are moving to deep decarbonization, one challenge is to balance supply and demand on a longer duration. Despite great progresses, current battery storage technology can at best achieve daily balancing but is prohibitively expensive for monthly or seasonal balancing. This paper discusses the potential of a cross-seasonal balancing option, which is a cross-seasonal load shift by firms in the industrial sector (or seasonal demand flexibility).

AbstractStructural health monitoring in the context of a Micon 65/13 horizontal axis wind turbine was described in this paper as a process in statistical pattern recognition. Simulation data from a calibrated model with less than 8% error in the first 14 natural frequencies of vibration was used to study the operational response under various wind states as well as the effects of three types of damage in the blade, low speed shaft and yaw joint. It was shown that vertical wind shear and turbulent winds lead to different modal contributions in the operational response of the turbine suggesting that the sensitivity of operational data to damage depends on the wind loads. It is also shown that there is less than a 4% change in the wind turbine natural frequencies given a 25% reduction in the stiffness at the root of one blade. The modal assurance criterion was used to analyse the corresponding changes in modal deflections, and this criterion exhibited nearly orthogonal changes because of the three damage scenarios suggesting that the modal deflection determines which damage is observable at a given frequency for a given wind state. The' modal contribution is calculated as a damage feature, which changes as much as 100% for 50% reductions in blade root stiffness, but only the blade damage is detected using this feature. Operational data was used to study variations in the forced blade response to determine the likelihood that small levels of damage can be detected amidst variations in wind speed across the rotor plane. The standard deviation in measured data was shown to be smallest for the span and edge‐wise measurements at 1P due to gravity, which provides the dominant forcing function at this frequency. A 3% change in the response in the span and edge‐wise directions because of damage is required to detect a change of three standard deviations in contrast to the 90% change in flap direction response that is required to detect a similar change because of damage. The dynamic displacement in the span direction is then used to extract a damage feature from the simulation data that provides the ability to both locate and quantify the reduction in stiffness in the blade root. Copyright © 2011 John Wiley & Sons, Ltd.

Photovoltaic (PV) systems play an important role in contemporary electricity production as a ubiquitous renewable energy source. However, the performance of a PV system is susceptible to unexpected faults that occur inside its various components. In this paper, we propose a quickest fault detection algorithm for PV systems under the sequential change detection framework. In particular, multiple meters are employed to measure different output signals of the PV system. The time correlation of the faulty signal and the signal correlation among different meters are exploited by a vector AR model in modeling the post-change signal. In order to tackle the difficulty that no prior knowledge about the fault is available, we develop a change detection algorithm based on the generalized local likelihood ratio test. Extensive simulation results demonstrate that the proposed method achieves high adaptivity and fast detection in dealing with various types of faults in PV systems.

A growing partisan divide in Congress stalled almost all new federal climate policy in 2011. The divide frustrated efforts to pass a cap-and-trade carbon permitting system, spawned a battle between the US Environmental Protection Agency (EPA) and Congress, pushed most substantive climate change policy down to the municipal level and hindered US ability to effectively negotiate an international climate agreement. Amid the federal partisan wrangling, US cities have enacted far-sighted climate policy initiatives, and the growing cost of fossil fuels has stimulated investment in renewable energy, edging the country closer to commercially viable alternatives to fossil fuels. These trends could help provide an alternate route to climate mitigation, even without international treaties or national legislation. But the inevitable shift from fossil fuels to renewable energy sources would be greatly hastened by federal action to tax carbon dioxide emissions and use the revenue generate! d to support alternative energy technologies. That action is extremely unlikely to occur unless climate change comes to be seen in the United States as a practical, rather than ideological, issue.

Abstract Within an offshore wind park, wind flow characteristics are quite complex and govern both the energy production and the structural wind turbine response. An experimental study focussed on assessing the spatial variability of winds near the German offshore wind energy platform FINO1 was conducted using multiple remote sensing devices. This study focuses on measuring the wind turbine wake characteristics, such as velocity deficit, the extent (length and width) of the wake and wake meandering under various atmospheric conditions using the data collected from a single scanning Doppler Lidar for several months in 2016. A new algorithm based on using a Gaussian model to measure the downwind wake characteristics is developed. The wind turbine downwind wake deficits compared well to previous models at far-wake regions, while at near-wake regions the models deviated due to different instruments & methodologies used in measuring the wake characteristics. It was also observed that the length of the Alpha Ventus wind turbine wake varied from 3 to 15 times the Rotor Diameter (RD), and the maximum velocity deficit varied from 55% to 75% of the free-stream wind speed, depending on mean wind speed and atmospheric stability. Detailed analysis of the Alpha Ventus wind turbine wake characteristics is presented.

Abstract Recently, blockchain adoption in prosumer-side energy trading has been actively studied. However, most of the conventional frameworks permanently store all transactions which increases blockchain management cost and reduces the user privacy. Additionally, most of the existing solutions focus on facilitating energy trading and negotiation, while ignoring two critical issues: data acquisition and contract execution. The former refers to the process of collecting power generation/consumption information from on-site energy resources which is required to scale. The latter refers to the process of adjusting controllable loads’ operation in real-time. In this paper, we propose a removable blockchain architecture that introduces a Temporary Chain (TC) where transactions can be stored for a particular period of time. The architecture enables an energy manager node to effectively collect data for facilitating real-time load control. TC reduces the volume of transactions stored in blockchain which increases scalability, throughput, and privacy of the users and reduces latency. We present two approaches to implement TC which are: i) blackboard where a central authority stores temporary transactions, and ii) removable ledger. We introduce a lightweight mode to transfer data. The implementation results show that the proposed framework reduces blockchain storage size and delay and increases throughput.