London 2012: changing delivery patterns in response to the impact of the Games on traffic flows

The paper addresses road freight transport operations during the London Olympic and Paralympic Games in 2012. It presents work carried out prior to the Games to understand pre-Games patterns of freight deliveries in London (for both light and heavy goods vehicles) and the results of modelling work carried out to assess the likely impacts of the Games road restrictions on freight operations. The modelling results indicated that increases in total hours travelled carrying out collection and delivery work would range from 1.4% to 11.4% in the six sectors considered. The results suggested increases in hours travelled in excess of 3.5% in four of the six sectors modelled. The possible actions that could be taken by organizations to reduce these negative impacts were also modelled and the results indicated that such actions would help to mitigate the impact of the road restrictions imposed on operators during the Games. The actual impacts of the 2012 Games on transport both in general terms and specifically in terms of freight transport are also discussed, together with the success of the actions taken by Transport for London (TfL) to help the road freight industry. The potential freight transport legacy of the London 2012 Games in terms of achieving more sustainable urban freight transport is considered and the steps being taken by TfL to help ensure that such a legacy can be realized are discussed. Such steps include policy-makers continuing to collaborate closely with the freight industry through the ‘London Freight Forum’, and TfL's efforts to encourage and support companies revising their delivery and collection times to the off-peak; improving freight planning in the design and management of TfL-funded road schemes; electronic provision of traffic information by TfL to the freight industry, and the further development of freight journey planning tools.

Particle swarm optimization based approaches to vehicle-to-grid scheduling

This paper addresses the problem of energy resources management using modern metaheuristics approaches, namely Particle Swarm Optimization (PSO), New Particle Swarm Optimization (NPSO) and Evolutionary Particle Swarm Optimization (EPSO). The addressed problem in this research paper is intended for aggregators’ use operating in a smart grid context, dealing with Distributed Generation (DG), and gridable vehicles intelligently managed on a multi-period basis according to its users’ profiles and requirements. The aggregator can also purchase additional energy from external suppliers. The paper includes a case study considering a 30 kV distribution network with one substation, 180 buses and 90 load points. The distribution network in the case study considers intense penetration of DG, including 116 units from several technologies, and one external supplier. A scenario of 6000 EVs for the given network is simulated during 24 periods, corresponding to one day. The results of the application of the PSO approaches to this case study are discussed deep in the paper.

Intelligent energy resource management considering vehicle-to-grid: A simulated annealing approach

This paper proposes a simulated annealing (SA) approach to address energy resources management from the point of view of a virtual power player (VPP) operating in a smart grid. Distributed generation, demand response, and gridable vehicles are intelligently managed on a multiperiod basis according to V2G user´s profiles and requirements. Apart from using the aggregated resources, the VPP can also purchase additional energy from a set of external suppliers. The paper includes a case study for a 33 bus distribution network with 66 generators, 32 loads, and 1000 gridable vehicles. The results of the SA approach are compared with a methodology based on mixed-integer nonlinear programming. A variation of this method, using ac load flow, is also used and the results are compared with the SA solution using network simulation. The proposed SA approach proved to be able to obtain good solutions in low execution times, providing VPPs with suitable decision support for the management of a large number of distributed resources.

Day-ahead resource scheduling in smart grids considering Vehicle-to-Grid and network constraints

Energy resource scheduling becomes increasingly important, as the use of distributed resources is intensified and massive gridable vehicle use is envisaged. The present paper proposes a methodology for dayahead energy resource scheduling for smart grids considering the intensive use of distributed generation and of gridable vehicles, usually referred as Vehicle- o-Grid (V2G). This method considers that the energy resources are managed by a Virtual Power Player (VPP) which established contracts with V2G owners. It takes into account these contracts, the user´s requirements subjected to the VPP, and several discharge price steps. Full AC power flow calculation included in the model allows taking into account network constraints. The influence of the successive day requirements on the day-ahead optimal solution is discussed and considered in the proposed model. A case study with a 33 bus distribution network and V2G is used to illustrate the good performance of the proposed method.

Application-specific modified particle swarm optimization for energy resource scheduling considering vehicle-to-grid

This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding he management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.

Decision support tool for Virtual Power Players: Hybrid Particle Swarm Optimization applied to Day-ahead Vehicle-To-Grid Scheduling

This paper presents a decision support tool methodology to help virtual power players (VPPs) in the Smart Grid (SGs) context to solve the day-ahead energy resource scheduling considering the intensive use of Distributed Generation (DG) and Vehicle-To-Grid (V2G). The main focus is the application of a new hybrid method combing a particle swarm approach and a deterministic technique based on mixedinteger linear programming (MILP) to solve the day-ahead scheduling minimizing total operation costs from the aggregator point of view. A realistic mathematical formulation, considering the electric network constraints and V2G charging and discharging efficiencies is presented. Full AC power flow calculation is included in the hybrid method to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.

Application-Specific Modified Particle Swarm Optimization for energy resource scheduling considering vehicle-to-grid

This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding the management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.

Bone markers and cytokines in response to low-impact, high-intensity exercise

A low-impact, high-intensity interval exercise (HIE) bout was used to determine whether an association exists between cytokines and bone turnover markers following an acute bout of exercise. Twenty-three recreationally active males (21.8±2.4yr) performed a single HIE bout on a cycle ergometer at 90% relative intensity. Venous blood samples were collected prior to exercise, 5-minutes, 1-hour, and 24-hours post-exercise, and were analyzed for serum levels of pro-inflammatory (IL-6, IL-1α, IL-1β, and TNF-α) and anti- inflammatory cytokines (IL-10) and markers of bone formation (BAP, OPG) and resorption (NTX, RANKL). Significant effects were observed with all bone markers, especially 5-minutes post-exercise with BAP, OPG, and RANKL increasing from baseline (p<0.05). Significant effects were also observed for IL-1α, IL-1β, IL-6, and TNF-α (p<0.00, p=0.04, p=0.03, p<0.00). In addition, post-exercise changes in NTX, BAP, and OPG were significantly correlated pro- and anti-inflammatory cytokines, suggesting that an interaction exists between the immune and skeletal response to exercise.

Phyto-activity of biocides used to manipulate herbivory: tests of three pesticides on fourteen plant species

1. We tested three pesticides used for field manipulations of herbivory for direct phytoactive effects on the germination and growth of 14 herbaceous plant species selected to provide a range of life-history strategies and functional groups. 2. We report three companion experiments: (A) Two insecticides, chlorpyrifos (granular soil insecticide) and dimethoate (foliar spray), were applied in fully-factorial combination to pot-germinated individuals of 12 species. (B) The same fully-factorial design was used to test for direct effects on the germination of four herbaceous legumes. (C) The molluscicide, metaldehyde, was tested for direct effects on the germination and growth of six plant species. 3. The insecticides had few significant effects on growth and germination. Dimethoate acted only on growth stimulating Anisantha sterilis, Sonchus asper and Stellaria graminea. In contrast, chlorpyrifos acted on germination increasing the germination of Trifolium dubium and Trifolium pratense. There was also a significant interactive effect of chlorpyrifos and dimethoate on the germination of T pratense. However, all. effects were relatively small in magnitude and explanatory power. The molluscicide had no significant effect on plant germination or growth. 4. The small number and size of direct effects of the pesticides on plant performance is encouraging for the use of these pesticides in manipulative experiments on herbivory, especially for the molluscicide. However, a smatt number of direct (positive) effects of the insecticides on some plant species need to be taken into account when interpreting field manipulations of herbivory with these compounds, and emphasises the importance of conducting tests for direct phyto-active effects. (C) 2004 Elsevier GmbH. All rights reserved.

The use of land-sea warming contrast under climate change to improve impact metrics

A favoured method of assimilating information from state-of-the-art climate models into integrated assessment models of climate impacts is to use the transient climate response (TCR) of the climate models as an input, sometimes accompanied by a pattern matching approach to provide spatial information. More recent approaches to the problem use TCR with another independent piece of climate model output: the land-sea surface warming ratio (φ). In this paper we show why the use of φ in addition to TCR has such utility. Multiple linear regressions of surface temperature change onto TCR and φ in 22 climate models from the CMIP3 multi-model database show that the inclusion of φ explains a much greater fraction of the inter-model variance than using TCR alone. The improvement is particularly pronounced in North America and Eurasia in the boreal summer season, and in the Amazon all year round. The use of φ as the second metric is beneficial for three reasons: firstly it is uncorrelated with TCR in state-of-the-art climate models and can therefore be considered as an independent metric; secondly, because of its projected time-invariance, the magnitude of φ is better constrained than TCR in the immediate future; thirdly, the use of two variables is much simpler than approaches such as pattern scaling from climate models. Finally we show how using the latest estimates of φ from climate models with a mean value of 1.6—as opposed to previously reported values of 1.4—can significantly increase the mean time-integrated discounted damage projections in a state-of-the-art integrated assessment model by about 15 %. When compared to damages calculated without the inclusion of the land-sea warming ratio, this figure rises to 65 %, equivalent to almost 200 trillion dollars over 200 years.