Control methodologies: Peak reduction algorithms for DNO owned storage devices on the Low Voltage network

The Distribution Network Operators (DNOs) role is becoming more difficult as electric vehicles and electric heating penetrate the network, increasing the demand. As a result it becomes harder for the distribution networks infrastructure to remain within its operating constraints. Energy storage is a potential alternative to conventional network reinforcement such as upgrading cables and transformers. The research presented here in this paper shows that due to the volatile nature of the LV network, the control approach used for energy storage has a significant impact on performance. This paper presents and compares control methodologies for energy storage where the objective is to get the greatest possible peak demand reduction across the day from a pre-specified storage device. The results presented show the benefits and detriments of specific types of control on a storage device connected to a single phase of an LV network, using aggregated demand profiles based on real smart meter data from individual homes. The research demonstrates an important relationship between how predictable an aggregation is and the best control methodology required to achieve the objective.

A peak reduction scheduling algorithm for storage devices on the low voltage network

Reinforcing the Low Voltage (LV) distribution network will become essential to ensure it remains within its operating constraints as demand on the network increases. The deployment of energy storage in the distribution network provides an alternative to conventional reinforcement. This paper presents a control methodology for energy storage to reduce peak demand in a distribution network based on day-ahead demand forecasts and historical demand data. The control methodology pre-processes the forecast data prior to a planning phase to build in resilience to the inevitable errors between the forecasted and actual demand. The algorithm uses no real time adjustment so has an economical advantage over traditional storage control algorithms. Results show that peak demand on a single phase of a feeder can be reduced even when there are differences between the forecasted and the actual demand. In particular, results are presented that demonstrate when the algorithm is applied to a large number of single phase demand aggregations that it is possible to identify which of these aggregations are the most suitable candidates for the control methodology.

Folding of graphene slit like pore walls—a simple method of improving CO2 separation from mixtures with CH4 or N2

We report for the first time a detailed procedure for creating a simulation model of energetically stable, folded graphene-like pores and simulation results of CO2/CH4 and CO2/N2 separation using these structures. We show that folding of graphene structures is a very promising method to improve the separation of CO2 from mixtures with CH4 and N2. The separation properties of the analyzed materials are compared with carbon nanotubes having similar diameters or S/V ratio. The presented results have potential importance in the field of CO2 capture and sequestration.

Effective separation of Am(III) and Eu(III) from HNO3 Q1 Q2 solutions using CyMe4-BTPhen-functionalized silica-coated magnetic nanoparticles

It has been shown that CyMe4-BTPhen-functionalized silica-coated maghemite (c-Fe2O3) magnetic nanoparticles (MNPs) are capable of quantitative separation of Am(III) from Eu(III) from HNO3 solutions. These MNPs also show a small but significant selectivity for Am(III) over Cm(III) with a separation factor of around 2 in 4 M HNO3. The water molecule in the cavity of the BTPhen may also play an important part in the selectivity.

An analysis of the accuracy of magnetopause reconnection rate variations deduced from cusp ion dispersion characteristics

We present an analysis of the accuracy of the method introduced by Lockwood et al. (1994) for the determination of the magnetopause reconnection rate from the dispersion of precipitating ions in the ionospheric cusp region. Tests are made by applying the method to synthesised data. The simulated cusp ion precipitation data are produced by an analytic model of the evolution of newly-opened field lines, along which magnetosheath ions are firstly injected across the magnetopause and then dispersed as they propagate into the ionosphere. The rate at which these newly opened field lines are generated by reconnection can be varied. The derived reconnection rate estimates are then compared with the input variation to the model and the accuracy of the method assessed. Results are presented for steady-state reconnection, for continuous reconnection showing a sine-wave variation in rate and for reconnection which only occurs in square wave pulses. It is found that the method always yields the total flux reconnected (per unit length of the open-closed field-line boundary) to within an accuracy of better than 5%, but that pulses tend to be smoothed so that the peak reconnection rate within the pulse is underestimated and the pulse length is overestimated. This smoothing is reduced if the separation between energy channels of the instrument is reduced; however this also acts to increase the experimental uncertainty in the estimates, an effect which can be countered by improving the time resolution of the observations. The limited time resolution of the data is shown to set a minimum reconnection rate below which the method gives spurious short-period oscillations about the true value. Various examples of reconnection rate variations derived from cusp observations are discussed in the light of this analysis.

Effective separation of Am(III) and Eu(III) from HNO3 solutions using CyMe4-BTPhen-functionalized silica-coated magnetic nanoparticles

It has been shown that CyMe4-BTPhen-functionalized silica-coated maghemite (c-Fe2O3) magnetic nanoparticles (MNPs) are capable of quantitative separation of Am(III) from Eu(III) from HNO3 solutions. These MNPs also show a small but significant selectivity for Am(III) over Cm(III) with a separation factor of around 2 in 4 M HNO3. The water molecule in the cavity of the BTPhen may also play an important part in the selectivity.

Tuning thermal properties and microphase separation in aliphatic polyester ABA copolymers

Four alkyl substituted β-lactones were investigated as monomers in ring opening polymerisation to produce a family of poly(3-hydroxyalkanoate)s. Homopolymers were synthesised using a robust aluminium salen catalyst, resulting in polymers with low dispersity (Đ < 1.1) and predictable molecular weights. ABA triblock copolymers were prepared using poly(L-lactic acid) as the A block and the aforementioned poly(3-hydroxyalkanoate) as the B block via a sequential addition method. Characterisation of these copolymers determined they were well controlled with low dispersities and predictable molecular weight. DSC analysis determined copolymers prepared from β-butyrolactone or β-valerolactone yielded polymers with tunable and predictable thermal properties. Copolymers prepared from β-heptanolactone yielded a microphase separated material as indicated by SAXS, with two distinct Tgs. The polymers could be readily cast into flexible films and their improved tensile properties were explored.

Effective separation of the actinides Am(III) and Cm(III) by electronic modulation of bis-(1,2,4-triazin-3-yl)phenanthrolines

It has been shown that modification of the phenanthroline backbone of CyMe4-BTPhen leads to subtle electronic modulation, permitting differential ligation of Am(III) and Cm(III) resulting in separation factors up to 7.

Evaluating perceptual separation in a pilot system for affective composition

Research evaluating perceptual responses to music has identified many structural features as correlates that might be incorporated in computer music systems for affectively charged algorithmic composition and/or expressive music performance. In order to investigate the possible integration of isolated musical features to such a system, a discrete feature known to correlate some with emotional responses – rhythmic density – was selected from a literature review and incorporated into a prototype system. This system produces variation in rhythm density via a transformative process. A stimulus set created using this system was then subjected to a perceptual evaluation. Pairwise comparisons were used to scale differences between 48 stimuli. Listener responses were analysed with Multidimensional scaling (MDS). The 2-Dimensional solution was then rotated to place the stimuli with the largest range of variation across the horizontal plane. Stimuli with variation in rhythmic density were placed further from the source material than stimuli that were generated by random permutation. This, combined with the striking similarity between the MDS scaling and that of the 2-dimensional emotional model used by some affective algorithmic composition systems, suggests that isolated musical feature manipulation can now be used to parametrically control affectively charged automated composition in a larger system.

Peak residential electricity demand and social practices: Deriving flexibility and greenhouse gas intensities from time use and locational data

Peak residential electricity demand takes place when people conduct simultaneous activities at specific times of the day. Social practices generate patterns of demand and can help understand why, where, with whom and when energy services are used at peak time. The aim of this work is to make use of recent UK time use and locational data to better understand: (i) how a set of component indices on synchronisation, variation, sharing and mobility indicate flexibility to shift demand; and (ii) the links between people’s activities and peaks in greenhouse gases’ intensities. The analysis is based on a recent UK time use dataset, providing 1 minute interval data from GPS devices and 10 minute data from diaries and questionnaires for 175 data days comprising 153 respondents. Findings show how greenhouse gases’ intensities and flexibility to shift activities vary throughout the day. Morning peaks are characterised by high levels of synchronisation, shared activities and occupancy, with low variation of activities. Evening peaks feature low synchronisation, and high spatial mobility variation of activities. From a network operator perspective, the results indicate that periods with lower flexibility may be prone to more significant local network loads due to the synchronization of electricity-demanding activities.

Microphase separation induced in the melt of Pluronic copolymers by blending with a hydrogen bonding urea–urethane end-capped supramolecular polymer

Blending with a hydrogen-bonding supramolecular polymer is shown to be a successful novel strategy to induce microphase-separation in the melt of a Pluronic polyether block copolymer. The supramolecular polymer is a polybutadiene derivative with urea–urethane end caps. Microphase separation is analysed using small-angle X-ray scattering and its influence on the macroscopic rheological properties is analysed. FTIR spectroscopy provides a detailed picture of the inter-molecular interactions between the polymer chains that induces conformational changes leading to microphase separation.

Challenges in quantifying wind generation's contribution to securing peak demand

Wind generation's contribution to supporting peak electricity demand is one of the key questions in wind integration studies. Differently from conventional units, the available outputs of different wind farms cannot be approximated as being statistically independent, and hence near-zero wind output is possible across an entire power system. This paper will review the risk model structures currently used to assess wind's capacity value, along with discussion of the resulting data requirements. A central theme is the benefits from performing statistical estimation of the joint distribution for demand and available wind capacity, focusing attention on uncertainties due to limited histories of wind and demand data; examination of Great Britain data from the last 25 years shows that the data requirements are greater than generally thought. A discussion is therefore presented into how analysis of the types of weather system which have historically driven extreme electricity demands can help to deliver robust insights into wind's contribution to supporting demand, even in the face of such data limitations. The role of the form of the probability distribution for available conventional capacity in driving wind capacity credit results is also discussed.

Decadal predictability of regional-scale peak winds over Europe using the Earth System Model of the Max-Planck-Institute for Meteorology

The predictability of high impact weather events on multiple time scales is a crucial issue both in scientific and socio-economic terms. In this study, a statistical-dynamical downscaling (SDD) approach is applied to an ensemble of decadal hindcasts obtained with the Max-Planck-Institute Earth System Model (MPI-ESM) to estimate the decadal predictability of peak wind speeds (as a proxy for gusts) over Europe. Yearly initialized decadal ensemble simulations with ten members are investigated for the period 1979–2005. The SDD approach is trained with COSMO-CLM regional climate model simulations and ERA-Interim reanalysis data and applied to the MPI-ESM hindcasts. The simulations for the period 1990–1993, which was characterized by several windstorm clusters, are analyzed in detail. The anomalies of the 95 % peak wind quantile of the MPI-ESM hindcasts are in line with the positive anomalies in reanalysis data for this period. To evaluate both the skill of the decadal predictability system and the added value of the downscaling approach, quantile verification skill scores are calculated for both the MPI-ESM large-scale wind speeds and the SDD simulated regional peak winds. Skill scores are predominantly positive for the decadal predictability system, with the highest values for short lead times and for (peak) wind speeds equal or above the 75 % quantile. This provides evidence that the analyzed hindcasts and the downscaling technique are suitable for estimating wind and peak wind speeds over Central Europe on decadal time scales. The skill scores for SDD simulated peak winds are slightly lower than those for large-scale wind speeds. This behavior can be largely attributed to the fact that peak winds are a proxy for gusts, and thus have a higher variability than wind speeds. The introduced cost-efficient downscaling technique has the advantage of estimating not only wind speeds but also estimates peak winds (a proxy for gusts) and can be easily applied to large ensemble datasets like operational decadal prediction systems.

Colloidal gas aphrons based separation process for the purification and fractionation of natural phenolic extracts

Following previous studies, the aim of this work is to further investigate the application of colloidal gas aphrons (CGA) to the recovery of polyphenols from a grape marc ethanolic extract with particular focus on exploring the use of a non-ionic food grade surfactant (Tween 20) as an alternative to the more toxic cationic surfactant CTAB. Different batch separation trials in a flotation column were carried out to evaluate the influence of surfactant type and concentration and processing parameters (such as pH, drainage time, CGA/extract volumetric and molar ratio) on the recovery of total and specific phenolic compounds. The possibility of achieving selective separation and concentration of different classes of phenolic compounds and non-phenolic compounds was also assessed, together with the influence of the process on the antioxidant capacity of the recovered compounds. The process led to good recovery, limited loss of antioxidant capacity, but low selectivity under the tested conditions. Results showed the possibility of using Tween 20 with a separation mechanism mainly driven by hydrophobic interactions. Volumetric ratio rather than the molar ratio was the key operating parameter in the recovery of polyphenols by CGA.