Current methods and advances in forecasting of wind power generation

Wind power generation differs from conventional thermal generation due to the stochastic nature of wind. Thus wind power forecasting plays a key role in dealing with the challenges of balancing supply and demand in any electricity system, given the uncertainty associated with the wind farm power output. Accurate wind power forecasting reduces the need for additional balancing energy and reserve power to integrate wind power. Wind power forecasting tools enable better dispatch, scheduling and unit commitment of thermal generators, hydro plant and energy storage plant and more competitive market trading as wind power ramps up and down on the grid. This paper presents an in-depth review of the current methods and advances in wind power forecasting and prediction. Firstly, numerical wind prediction methods from global to local scales, ensemble forecasting, upscaling and downscaling processes are discussed. Next the statistical and machine learning approach methods are detailed. Then the techniques used for benchmarking and uncertainty analysis of forecasts are overviewed, and the performance of various approaches over different forecast time horizons is examined. Finally, current research activities, challenges and potential future developments are appraised. (C) 2011 Elsevier Ltd. All rights reserved.

Distance limits to intermediate- and high-velocity clouds

We present optical spectra of 403 stars and quasi-stellar objects in order to obtain distance limits towards intermediate- and high-velocity clouds (IHVCs), including new Fibre-fed Extended Range Optical Spectrograph (FEROS) observations plus archival ELODIE, FEROS, High Resolution Echelle Spectrometer (HIRES) and Ultraviolet and Visual Echelle Spectrograph (UVES) data. The non-detection of Ca II K interstellar (IS) absorption at a velocity of −130 to −60 km s−1 towards HDE 248894 (d ∼ 3 kpc) and HDE 256725 (d ∼ 8 kpc) in data at signal-to-noise ratio (S/N) > 450 provides a new firm lower distance limit of 8 kpc for the anti-centre shell HVC. Similarly, the non-detection of Ca II K IS absorption towards HD 86248 at S/N ∼ 500 places a lower distance limit of 7.6 kpc for Complex EP, unsurprising since this feature is probably related to the Magellanic System. The lack of detection of Na I D at S/N = 35 towards Mrk 595 puts an improved upper limit for the Na I column density of log (NNaD <) 10.95 cm−2 towards this part of the Cohen Stream where Ca II was detected by Wakker et al. Absorption at ∼ −40 km s−1 is detected in Na I D towards the Galactic star PG 0039+049 at S/N = 75, placing a firm upper distance limit of 1 kpc for the intermediate-velocity cloud south (IVS), where a tentative detection had previously been obtained by Centurion et al. Ca ´ II K and Na I D absorption is detected at −53 km s−1 towards HD 93521, which confirms the upper distance limit of 2.4 kpc for part of the IV arch complex obtained using the International Ultraviolet Explorer (IUE) data by Danly. Towards HD 216411 in Complex H a non-detection in Na D towards gas with log(NH I) = 20.69 cm−2 puts a lower distance limit of 6.6 kpc towards this HVC complex. Additionally, Na I D absorption is detected at −43.7 km s−1 in the star HD 218915 at a distance of 5.0 kpc in gas in the same region of the sky as Complex H. Finally, the Na I/Ca II and Ca II/H I ratios of the current sample are found to lie in the range observed for previous studies of IHVCs.

VLSI processor for high-performance arithmetic computations

A high performance VLSI architecture to perform combined multiply-accumulate, divide, and square root operations is proposed. The circuit is highly regular, requires only minimal control, and can be pipelined right down to the bit level. The system can also be reconfigured on every cycle to perform one or more of these operations. The throughput rate for each operation is the same and is wordlength independent. This is achieved using redundant arithmetic. With current CMOS technology, throughput rates in excess of 80 million operations per second are expected.

Effect of impeller speed on mechanical and dissolution properties of high-shear granules

Impeller speed is one of the most crucial process variables that affect the properties of the granules produced in a high-shear granulator. Several reports can be found in literature that discuss the influence of impeller speed on the granules size. For instance some researchers like Knight report an increase of granule size with impeller speed [1] and [2], while others (Scheaefer et al. and Ramaker et al.) observed a decrease of granules size with increasing impeller speed [3] and [4]. However there is limited work reported in literature on the effect of the impeller speed on the mechanical properties of granules. Mechanical properties are important as they affect the performance of the granules on the other downstream process such as transportation and handling. The work reported here serves to address the missing in knowledge gap regarding the influence of impeller speed on mechanical properties granules. How the granulation system responds to the changes in the impeller speeds depends on binder that is used in the process. For this reason the two extreme cases, of a low viscosity binder system and high viscosity binder system are considered in this research. For low viscosity binder system it was observed that the granule size decreased with increasing impeller speed whilst for the high viscosity binder system the opposite was observed by Knight [1]. The granule strength, the Young's modulus and yield strength of the high viscosity granules increased with increasing impeller speed where as the opposite trends were observed for the low viscosity binder granules.

Offshore wind power forecasting error and electricity market implications

Wind energy has been identified as key to the European Union’s 2050 low carbon economy. However, as wind is a variable resource and stochastic by nature, it is difficult to plan and schedule the power system under varying wind power generation. This paper investigates the impacts of offshore wind power forecast error on the operation and management of a pool-based electricity market in 2050. The impact of the magnitude and variance of the offshore wind power forecast error on system generation costs, emission costs, dispatch-down of wind, number of start-ups and system marginal price is analysed. The main findings of this research are that the magnitude of the offshore wind power forecast error has the largest impact on system generation costs and dispatch-down of wind, but the variance of the offshore wind power forecast error has the biggest impact on emissions costs and system marginal price. Overall offshore wind power forecast error variance results in a system marginal price increase of 9.6% in 2050.

Power-Capped DVFS and Thread Allocation with ANN Models on Modern NUMA Systems

Abstract—Power capping is an essential function for efficient power budgeting and cost management on modern server systems. Contemporary server processors operate under power caps by using dynamic voltage and frequency scaling (DVFS). However, these processors are often deployed in non-uniform memory
access (NUMA) architectures, where thread allocation between cores may significantly affect performance and power consumption. This paper proposes a method which maximizes performance under power caps on NUMA systems by dynamically optimizing two knobs: DVFS and thread allocation. The method selects the optimal combination of the two knobs with models based on artificial neural network (ANN) that captures the nonlinear effect of thread allocation on performance. We implement
the proposed method as a runtime system and evaluate it with twelve multithreaded benchmarks on a real AMD Opteron based NUMA system. The evaluation results show that our method outperforms a naive technique optimizing only DVFS by up to
67.1%, under a power cap.

A long-term analysis of pumped hydro storage to firm wind power

Although pumped hydro storage is seen as a strategic key asset by grid operators, financing it is complicated in new liberalised markets. It could be argued that the optimum generation portfolio is now determined by the economic viability of generators based on a short to medium term return on investment. This has meant that capital intensive projects such as pumped hydro storage are less attractive for wholesale electricity companies because the payback periods are too long. In tandem a significant amount of wind power has entered the generation mix, which has resulted in operating and planning integration issues due to wind's inherent uncertain, varying spatial and temporal nature. These integration issues can be overcome using fast acting gas peaking plant or energy storage. Most analysis of wind power integration using storage to date has used stochastic optimisation for power system balancing or arbitrage modelling to examine techno-economic viability. In this research a deterministic dynamic programming long term generation expansion model is employed to optimise the generation mix, total system costs and total carbon dioxide emissions, and unlike other studies calculates reserve to firm wind power. The key finding of this study is that the incentive to build capital-intensive pumped hydro storage to firm wind power is limited unless exogenous market costs come very strongly into play. Furthermore it was demonstrated that reserve increases with increasing wind power showing the importance of ancillary services in future power systems. © 2014 Elsevier Ltd. All rights reserved.

Enhanced AIMD based decentralized residential charging of EVs

Moving from combustion engine to electric vehicle (EV)-based transport is recognized as having a major role to play in reducing pollution, combating climate change and improving energy security. However, the introduction of EVs poses major challenges for power system operation. With increasing penetration of EVs, uncontrolled coincident charging may overload the grid and substantially increase peak power requirements. Developing smart grid technologies and appropriate charging strategies to support the role out of EVs is therefore a high priority. In this paper, we investigate the effectiveness of distributed additive increase and multiplicative decrease (AIMD) charging algorithms, as proposed by Stu¨dli et al. in 2012, at mitigating the impact of domestic charging of EVs on low-voltage distribution networks. In particular, a number of enhancements to the basic AIMD implementation are introduced to enable local power system infrastructure and voltage level constraints to be taken into account and to reduce peak power requirements. The enhanced AIMD EV charging strategies are evaluated using power system simulations for a typical low-voltage residential feeder network in Ireland. Results show that by using the proposed AIMD-based smart charging algorithms, 50% EV penetration can be accommodated, compared with only 10% with uncontrolled charging, without exceeding network infrastructure constraints.

Distributed MPC for Frequency Regulation in Multi-Terminal HVDC Grids

High Voltage Direct Current (HVDC) lines allow large quantities of power to be
transferred between two points in an electrical power system. A Multi-Terminal HVDC (MTDC) grid consists of a meshed network of HVDC lines, and this allows energy reserves to be shared between a number of AC areas in an efficient manner. Secondary Frequency Control (SFC) algorithms return the frequencies in areas connected by AC or DC lines to their original setpoints after Primary Frequency Controllers have been called following a contingency. Where multiple
TSOs are responsible for different parts of a MTDC grid it may not be possible to implement SFC from a centralised location. Thus, in this paper a simple gain based distributed Model Predictive Control strategy is proposed for Secondary Frequency Control of MTDC grids which allows TSOs to cooperatively perform SFC without the need for centralised coordination.

Cognitive amplify-and-forward relay networks with beamforming under primary user power constraint over Nakagami-m fading channels

In this paper, we analyze the performance of cognitive amplify-and-forward (AF) relay networks with beamforming under the peak interference power constraint of the primary user (PU). We focus on the scenario that beamforming is applied at the multi-antenna secondary transmitter and receiver. Also, the secondary relay network operates in channel state information-assisted AF mode, and the signals undergo independent Nakagami-m fading. In particular, closed-form expressions for the outage probability and symbol error rate (SER) of the considered network over Nakagami-m fading are presented. More importantly, asymptotic closed-form expressions for the outage probability and SER are derived. These tractable closed-form expressions for the network performance readily enable us to evaluate and examine the impact of network parameters on the system performance. Specifically, the impact of the number of antennas, the fading severity parameters, the channel mean powers, and the peak interference power is addressed. The asymptotic analysis manifests that the peak interference power constraint imposed on the secondary relay network has no effect on the diversity gain. However, the coding gain is affected by the fading parameters of the links from the primary receiver to the secondary relay network

Computational scheduling methods for integrating plug-in electric vehicles with power systems: a review

Traditional internal combustion engine vehicles are a major contributor to global greenhouse gas emissions and other air pollutants, such as particulate matter and nitrogen oxides. If the tail pipe point emissions could be managed centrally without reducing the commercial and personal user functionalities, then one of the most attractive solutions for achieving a significant reduction of emissions in the transport sector would be the mass deployment of electric vehicles. Though electric vehicle sales are still hindered by battery performance, cost and a few other technological bottlenecks, focused commercialisation and support from government policies are encouraging large scale electric vehicle adoptions. The mass proliferation of plug-in electric vehicles is likely to bring a significant additional electric load onto the grid creating a highly complex operational problem for power system operators. Electric vehicle batteries also have the ability to act as energy storage points on the distribution system. This double charge and storage impact of many uncontrollable small kW loads, as consumers will want maximum flexibility, on a distribution system which was originally not designed for such operations has the potential to be detrimental to grid balancing. Intelligent scheduling methods if established correctly could smoothly integrate electric vehicles onto the grid. Intelligent scheduling methods will help to avoid cycling of large combustion plants, using expensive fossil fuel peaking plant, match renewable generation to electric vehicle charging and not overload the distribution system causing a reduction in power quality. In this paper, a state-of-the-art review of scheduling methods to integrate plug-in electric vehicles are reviewed, examined and categorised based on their computational techniques. Thus, in addition to various existing approaches covering analytical scheduling, conventional optimisation methods (e.g. linear, non-linear mixed integer programming and dynamic programming), and game theory, meta-heuristic algorithms including genetic algorithm and particle swarm optimisation, are all comprehensively surveyed, offering a systematic reference for grid scheduling considering intelligent electric vehicle integration.

Localization Algorithm Performance in Ultra Low Power Active RFID Based Patient Tracking

Indoor personnel localization research has generated a range of potential techniques and algorithms. However, these typically do not account for the influence of the user's body upon the radio channel. In this paper an active RFID based patient tracking system is demonstrated and three localization algorithms are used to estimate the location of a user within a modern office building. It is shown that disregarding body effects reduces the accuracy of the algorithms' location estimates and that body shadowing effects create a systematic position error that estimates the user's location as closer to the RFID reader that the active tag has line of sight to.

Design of an automated solar concentrator for the pyrolysis of scrap rubber

An automated solar reactor system was designed and built to carry out catalytic pyrolysis of scrap rubber tires at 550°C. To maximize solar energy concentration, a two degrees-of-freedom automated sun tracking system was developed and implemented. Both the azimuth and zenith angles were controlled via feedback from six photo-resistors positioned on a Fresnel lens. The pyrolysis of rubber tires was tested with the presence of two types of acidic catalysts, H-beta and H-USY. Additionally, a photoactive TiO<inf>2</inf> catalyst was used and the products were compared in terms of gas yields and composition. The catalysts were characterized by BET analysis and the pyrolysis gases and liquids were analyzed using GC-MS. The oil and gas yields were relatively high with the highest gas yield reaching 32.8% with H-beta catalyst while TiO<inf>2</inf> gave the same results as thermal pyrolysis without any catalyst. In the presence of zeolites, the dominant gasoline-like components in the gas were propene and cyclobutene. The TiO<inf>2</inf> and non-catalytic experiments produced a gas containing gasoline-like products of mainly isoprene (76.4% and 88.4% respectively). As for the liquids they were composed of numerous components spread over a wide distribution of C<inf>10</inf> to C<inf>29</inf> hydrocarbons of naphthalene and cyclohexane/ene derivatives.

The significance of interconnector counter-trading in a security constrained electricity market

Throughout the European Union there is an increasing amount of wind generation being dispatched-down due to the binding of power system operating constraints from high levels of wind generation. This paper examines the impact a system non-synchronous penetration limit has on the dispatch-down of wind and quantifies the significance of interconnector counter-trading to the priority dispatching of wind power. A fully coupled economic dispatch and security constrained unit commitment model of the Single Electricity Market of the Republic of Ireland and Northern Ireland and the British Electricity Trading and Transmission Arrangement was used in this study. The key finding was interconnector counter-trading reduces the impact the system non-synchronous penetration limit has on the dispatch-down of wind. The capability to counter-trade on the interconnectors and an increase in system non-synchronous penetration limit from 50% to 55% reduces the dispatch-down of wind by 311 GW h and decreases total electricity payments to the consumer by €1.72/MW h. In terms of the European Union electricity market integration, the results show the importance of developing individual electricity markets that allow system operators to counter-trade on interconnectors to ensure the priority dispatch of the increasing levels of wind generation.