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.

Intelligent demand response electric water heating to integrate wind power in Ireland

Dwindling fossil fuel resources and pressures to reduce greenhouse gas (GHG) emissions will result in a more diverse range of generation portfolios for future electricity systems. Irrespective of the portfolio mix the overarching requirement for all electricity suppliers and system operators is that supply instantaneously meets demand and that robust operating standards are maintained to ensure a consistent supply of high quality electricity to end-users. Therefore all electricity market participants will ultimately need to use a variety of tools to balance the power system. Thus the role of demand side management (DSM) with energy storage will be paramount to integrate future diverse generation portfolios. Electric water heating (EWH) has been studied previously, particularly at the domestic level to provide load control, peak shave and to benefit end-users financially with lower bills, particularly in vertically integrated monopolies. In this paper, a continuous Direct Load Control (DLC) EWH algorithm is applied in a liberalized market environment using actual historical electricity system and market data to examine the potential energy savings, cost reductions and electricity system operational improvements.

Integrating wind power using intelligent electric water heating

Dwindling fossil fuel resources and pressures to reduce greenhouse gas emissions will result in a more diverse range of generation portfolios for future electricity systems. Irrespective of the portfolio mix the overarching requirement for all electricity suppliers and system operators is to instantaneously meet demand, to operate to standards and reduce greenhouse gas emissions. Therefore all electricity market participants will ultimately need to use a variety of tools to balance the power system. Thus the role of demand side management with energy storage will be paramount to integrate future diverse generation portfolios. Electric water heating has been studied previously, particularly at the domestic level to provide load control, peak shave and to bene?t end-users ?nancially with lower bills, particularly in vertically integrated monopolies. In this paper a number of continuous direct load control demand response based electric water heating algorithms are modelled to test the effectiveness of wholesale electricity market signals to study the system bene?ts. The results are compared and contrasted to determine which control algorithm showed the best potential for energy savings, system marginal price savings and wind integration.

Density Functional study of the photoactive yellow protein's chromophore

We have investigated the structural and electronic properties of p-coumaric acid, the chromophore of the photoactive yellow protein (PYP), by means of first-principles molecular dynamics based on density functional theory (DFT). We have studied the chromophore both in the vacuum and in an extended model which includes the nearest residues in the binding pocket of PYP, as derived from crystallographic data. We have characterized the ground state of the isolated chromophore in its protonated and deprotonated forms and computed the energy barrier involved in the trans to cis isomerization process around the carbon-carbon double bond. A comparison of the optimized structures of the chromophore in the vacuum and in the extended protein model, both in the trans (ground state of PYP in the dark) and cis (first light-activated intermediate) configuration, shows how the protein environment affects the chromophore in the first step of the photocycle. Our model gives an energy storage of 25 kcal/mol associated with the trans-to-cia photoisomerization. Finally, we have elucidated the nature of the electronic excitation relevant for the photochemistry of PYP by means of time-dependent DFT calculations.

Comparative study on performances of trimethyl-sulfonium and trimethyl-ammonium based ionic liquids in molecular solvents as electrolyte for electrochemical double layer capacitors

The present work reports a comparative study on the performances of two bis[(trifluoromethyl)sulfonyl]imide-based protic (PIL) and aprotic (AIL) ionic liquids, namely, trimethyl-ammonium bis[(trifluoromethyl)sulfonyl]imide ([HN][TFSI], PIL) and trimethyl-sulfonium bis[(trifluoromethyl) sulfonyl]imide ([S][TFSI], AIL), as mixtures with three molecular solvents: gamma butyrolactone (?-BL), propylene carbonate (PC), and acetonitrile (ACN) as electrolytes for supercapacitor applications. After an analysis of their transport properties as a function of temperature, cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and galvanostatic charge-discharge measurements were conducted at 25 and -30 C to investigate the performance of these mixtures as electrolytes for supercapacitors using activated carbon as the electrode material. Surprisingly, for each solvent investigated, no significant differences were observed between the electrolytes based on the PIL and AIL in their electrochemical performance due to the presence or the absence of the labile proton. Furthermore, good specific capacitances were observed in the case of ?-BL-based electrolytes even at low temperature. Capacitances up to 131 and 80 F·g are observed for the case of the [S][TFSI] + ?-BL mixture at 25 and -30 C, respectively. This latter result is very promising particularly for the formulation of new environmentally friendly electrolytes within energy storage systems even at low temperatures. © 2013 American Chemical Society.

Phillips-Heffron Power System Model for SmartPark and Suppression of Interarea Oscillations

This paper investigates the use of plug-in parking lots (SmartPark) as integral energy storage to improve small-signal stability using plug-in electric vehicles (PEV). The paper establishes the Phillips-Heffron model of a power system for a SmartPark solution. Based on this model, SmartPark-based stabilisers have been designed based using phase compensation to improve power system oscillation stability. The effectiveness of stabilisation superimposed on the active and reactive power regulators is verified by simulations obtained from a multi-machine power system model with SmartPark and a large-scale wind farm inclusion.

Day-Ahead Optimal Charging/Discharging Scheduling for Electric Vehicles in Micro-Grids

A micro-grid is an autonomous system which can be operated and connected to an external system or isolated with the help of energy storage systems (ESSs). While the daily output of distributed generators (DGs) strongly depends on the temporal distribution of natural resources such as wind and solar, unregulated electric vehicle (EV) charging demand will deteriorate the imbalance between the daily load and generation curves. In this paper, a statistical model is presented to describe daily EV charging/discharging behaviour. An optimisation problem is proposed to obtain economic operation for the micro-grid based on this model. In day-ahead scheduling, with estimated information of power generation and load demand, optimal charging/discharging of EVs during 24 hours is obtained. A series of numerical optimization solutions in different scenarios is achieved by serial quadratic programming. The results show that optimal charging/discharging of EVs, a daily load curve can better track the generation curve and the network loss and required ESS capacity are both decreased. The paper also demonstrates cost benefits for EVs and operators.

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.

Optimal Scheduling Methods to Integrate Plug-In Electric Vehicles with the Power System: A Review

The introduction of the Tesla in 2008 has demonstrated to the public of the potential of electric vehicles in terms of reducing fuel consumption and green-house gas from the transport sector. It has brought electric vehicles back into the spotlight worldwide at a moment when fossil fuel prices were reaching unexpected high due to increased demand and strong economic growth. The energy storage capabilities from of fleets of electric vehicles as well as the potentially random discharging and charging offers challenges to the grid in terms of operation and control. Optimal scheduling strategies are key to integrating large numbers of electric vehicles and the smart grid. In this paper, state-of-the-art optimization methods are reviewed on scheduling strategies for the grid integration with electric vehicles. The paper starts with a concise introduction to analytical charging strategies, followed by a review of a number of classical numerical optimization methods, including linear programming, non-linear programming, dynamic programming as well as some other means such as queuing theory. Meta-heuristic techniques are then discussed to deal with the complex, high-dimensional and multi-objective scheduling problem associated with stochastic charging and discharging of electric vehicles. Finally, future research directions are suggested.

Sandwich nanoarchitecture of LiV3O8/graphene multilayer nanomembranes via layer-by-layer self-assembly for long-cycle-life lithium-ion battery cathodes

A lack of suitable high-performance cathode materials has become the major barrier to their applications in future advanced communication equipment and electric vehicle power systems. In this paper, we have developed a layer-by-layer self-assembly approach for fabricating a novel sandwich nanoarchitecture of multilayered LiV₃O₈ nanoparticle/graphene nanosheet (M-nLVO/GN) hybrid electrodes for potential use in high performance lithium ion batteries by using a porous Ni foam as a substrate. The prepared sandwich nanoarchitecture of M-nLVO/GN hybrid electrodes exhibited high performance as a cathode material for lithium-ion batteries, such as high reversible specific capacity (235 mA h g^-1 at a current density of 0.3 A g^-1), high coulombic efficiency (over 98%), fast rate capability (up to a current density of 10 A g^-1), and superior capacity retention during cycling (90% capacity retention with a current density of 0.3 A g^-1 after 300 cycles). Very significantly, this novel insight into the design and synthesis of sandwich nanoarchitecture would extend their application to various electrochemical energy storage devices, such as fuel cells and supercapacitors.

Shape stabilised phase change materials based on a high melt viscosity HDPE and paraffin waxes

Shape stabilised phase change materials (SSPCMs) based on a high density poly(ethylene)(hv-HDPE) with high (H-PW, Tm = 56–58 °C) and low (L-PW, Tm = 18–23 °C) melting point paraffin waxes were readily prepared using twin-screw extrusion. The thermo-physical properties of these materials were assessed using a combination of techniques and their suitability for latent heat thermal energy storage (LHTES) assessed. The melt processing temperature (160 °C) of the HDPE used was well below the onset of thermal decomposition of H-PW (220 °C), but above that for L-PW (130 °C), although the decomposition process extended over a range of 120 °C and the residence time of L-PW in the extruder was <30 s. The SSPCMs prepared had latent heats up to 89 J/g and the enthalpy values for H-PW in the respective blends decreased with increasing H-PW loading, as a consequence of co-crystallisation of H-PW and hv-HDPE. Static and dynamic mechanical analysis confirmed both waxes have a plasticisation effect on this HDPE. Irrespective of the mode of deformation (tension, flexural, compression) modulus and stress decreased with increased wax loading in the blend, but the H-PW blends were mechanically superior to those with L-PW.

Carbons, Ionic Liquids, and Quinones for Electrochemical Capacitors

Carbons are the main electrode materials used in supercapacitors, which are electrochemical energy storage devices with high power densities and long cycling lifetimes. However, increasing their energy density capacity will improve their potential for commercial implementation.
In this regard, the use of high surface area carbons and high voltage electrolytes are well known strategies to increase the attainable energy density, and lately ionic liquids have been explored as promising alternatives to current state of the art acetonitrile-based electrolytes. Also, in terms of safety and sustainability ionic liquids are attractive electrolyte materials for supercapacitors. In addition, it has been shown that the matching of the carbon pore size with the electrolyte ion size further increases the attainable electrochemical double layer (ECDL) capacitance and energy density.
The use of pseudocapacitive reactions can significantly increase the attainable energy density, and quinonic-based materials offer a potentially sustainable and cost effective research avenue for both the electrode and the electrolyte.
This perspective will provide an overview of the current state of the art research on supercapacitors based on combinations of carbons, ionic liquids and quinonic compounds, highlighting performances and challenges and discussing possible future research avenues. In this regard, current interest is mainly focused on strategies which may ultimately lead to commercially competitive sustainable high performance supercapacitors for different applications including those requiring mechanical flexibility and biocompatibility.

Encapsulation of phase change materials using rice-husk-char

This paper explored a new approach to prepare phase change microcapsules using carbon-based particles via Pickering emulsions for energy storage applications. Rice-husk-char, a by-product in biofuel production, containing 53.58 wt% of carbon was used as a model carbon-based material to encapsulate hexadecane. As a model phase change material, hexadecane was emulsified in aqueous suspensions of rice-husk-char nanoparticles. Water soluble polymers poly(diallyldimethyl-ammonium chloride) and poly(sodium styrene sulfonate) were used to fix the rice-husk-char nanoparticles on the emulsion droplets through layer-by-layer assembly to enhance the structural stability of the microcapsules. The microcapsules formed are composed of a thin shell encompassing a large core consisting of hexadecane. Thermal gravimetrical and differential scanning calorimeter analyses showed the phase change enthalpy of 80.9 kJ kg−1 or 120.0 MJ m−3. Design criteria of phase change microcapsules and preparation considerations were discussed in terms of desired applications. This work demonstrated possible utilisations of biomass-originated carbon-based material for thermal energy recovery and storage applications, which can be a new route of carbon capture and utilisation.

Review of virtual power plant applications for the power system management and V2G market development

Utilization of renewable energy sources and energy storage systems is increasing with fostering new policies on energy industries. However, the increase of distributed generation hinders the reliability of power systems. In order to stabilize them, a virtual power plant emerges as a novel power grid management system. The VPP has a role to make a participation of different distributed energy resources and energy storage systems. This paper defines core technology of the VPP which are demand response and ancillary service concerning about Korea, America and Europe cases. It also suggests application solutions of the VPP to V2G market for restructuring national power industries in Korea.