Stochastic Hosting Capacity in LV Distribution Networks

Hosting capacity is defined as the level of penetration that a particular technology can connect to a distribution network without causing power quality problems. In this work, we study the impact of solar photovoltaics (PV) on voltage rise. In most cases, the locations and sizes of the PV are not known in advance, so hosting capacity must be considered as a random variable. Most hosting capacity methods study the problem considering a large number of scenarios, many of which provide little additional information. We overcome this problem by studying only cases where voltage constraints are active, with results illustrating a reduction in the number of scenarios required by an order of magnitude. A linear power flow model is utilised for this task, showing excellent performance. The hosting capacity is finally studied as a function of the number of generators connected, demonstrating that assumptions about the penetration level will have a large impact on the conclusions drawn for a given network

Mitigating the Impact of Personal Vehicle Electrification: a Power Generation Perspective

The number of electric vehicles on the road in the UK is expected to rise quickly in the coming years, and this is likely to have an impact on the operation of the power grid. This paper first quantifies the consequences of allowing a completely electric fleet to charge freely, then considers whether there is a practical way in which the impacts can be mitigated. We predict that, with an entirely electric fleet, the UK power generation capacity would need to increase by 1/3. We show that it is possible to completely mitigate this with controlled charging, although substantial infrastructure would be required. However, we propose a simple scheme which could largely avoid the negative effect and does not require the creation of new infrastructure. We show that this reduces the projected increase in peak electricity demand by 80-99%

Clustering of Usage Profiles for Electric Vehicle Behaviour Analysis

Accurately predicting the behaviour of electric vehicles is going to be imperative for network operators. In order for vehicles to participate in either smart charging schemes or providing grid services, their availability and charge requirements must be forecasted. Their relative novelty means that data concerning electric vehicles is scarce and biased, however we have been collecting data on conventional vehicles for many years. This paper uses cluster analysis of travel survey data from the UK to identify typical conventional vehicle usage profiles. To this end, we determine the feature vector, introduce an appropriate distance metric, and choose a number of clusters. Five clusters are identified, and their suitability for electrification is discussed. A smaller data set of electric vehicles is then used to compare the current electric fleet behaviour with the conventional one

Conic optimisation for electric vehicle station smart charging with battery voltage constraints

This paper proposes a new convex optimisation strategy for coordinating electric vehicle charging, which accounts for battery voltage rise, and the associated limits on maximum charging power. Optimisation strategies for coordinating electric vehicle charging commonly neglect the increase in battery voltage which occurs as the battery is charged. However, battery voltage rise is an important consideration, since it imposes limits on the maximum charging power. This is particularly relevant for DC fast charging, where the maximum charging power may be severely limited, even at moderate state of charge levels. First, a reduced order battery circuit model is developed, which retains the nonlinear relationship between state of charge and maximum charging power. Using this model, limits on the battery output voltage and battery charging power are formulated as second-order cone constraints. These constraints are integrated with a linearised power flow model for three-phase unbalanced distribution networks. This provides a new multiperiod optimisation strategy for electric vehicle smart charging. The resulting optimisation is a second-order cone program, and thus can be solved in polynomial time by standard solvers. A receding horizon implementation allows the charging schedule to be updated online, without requiring prior information about when vehicles will arrive

NMR shim coil design utilising a rapid spherical harmonic calculation method

A rapid spherical harmonic calculation method is used for the design of Nuclear Magnetic Resonance shim coils. The aim is to design each shim such that it generates a field described purely by a single spherical harmonic. By applying simulated annealing techniques, coil arrangements are produced through the optimal positioning of current-carrying circular arc conductors of rectangular cross-section. This involves minimizing the undesirable harmonies in relation to a target harmonic. The design method is flexible enough to be applied for the production of coil arrangements that generate fields consisting significantly of either zonal or tesseral harmonics. Results are presented for several coil designs which generate tesseral harmonics of degree one

Direct Observation of High-Temperature Polaronic Behavior In Colossal Magnetoresistive Manganites

The temperature dependence of the electronic and atomic structure of the colossal magnetoresistive oxides $La_{1-x}Sr_{x}MnO_{3}$ (x = 0.3, 0.4) has been studied using core and valence level photoemission, x-ray absorption and emission, and extended x-ray absorption fine structure spectroscopy. A dramatic and reversible change of the electronic structure is observed on crossing the Curie temperature, including charge localization and spin moment increase of Mn, together with Jahn-Teller distortions, both signatures of polaron formation. Our data are also consistent with a phase-separation scenario.Comment: 5 pages, 4 figures, revte

Comparison of tumour-based (Petersen Index) and inflammation-based (Glasgow Prognostic Score) scoring systems in patients undergoing curative resection for colon cancer

After resection, it is important to identify colon cancer patients, who are at a high risk of recurrence and who may benefit from adjuvant treatment. The Petersen Index (PI), a prognostic model based on pathological criteria is validated in Dukes' B and C disease. Similarly, the modified Glasgow Prognostic Score (mGPS) based on biochemical criteria has also been validated. This study compares both the scores in patients undergoing curative resection of colon cancer. A total of 244 patients underwent elective resection between 1997 and 2005. The PI was constructed from pathological reports; the mGPS was measured pre-operatively. The median follow-up was 67 months (minimum 36 months) during which 109 patients died; 68 of them from cancer. On multivariate analysis of age, Dukes' stage, PI and mGPS, age (hazard ratio, HR, 1.74, P=0.001), Dukes' stage (HR, 3.63, P<0.001), PI (HR, 2.05, P=0.010) and mGPS (HR, 2.34, P<0.001) were associated independently with cancer-specific survival. Three-year cancer-specific survival rates for Dukes' B patients with the low-risk PI were 98, 92 and 82% for the mGPS of 0, 1 and 2, respectively (P<0.05). The high-risk PI population is small, in particular for Dukes' B disease (9%). The mGPS further stratifies those patients classified as low risk by the PI. Combining both the scoring systems could identify patients who have undergone curative surgery but are at high-risk of cancer-related death, therefore guiding management and trial stratification