Electric vehicles and renewable energy sources are collectively being developed as a synergetic implementation for smart grids. In this context, smart charging of electric vehicles and vehicle-to-grid technologies are seen as a way forward to achieve economic, technical and environmental benefits. The implementation of these technologies requires the cooperation of the end-electricity user, the electric vehicle owner, the system operator and policy makers. These stakeholders pursue different and sometime conflicting objectives. In this paper, the concept of multi-objective-techno-economicenvironmental optimisation is proposed for scheduling electric vehicle charging/discharging. End user energy cost, battery degradation, grid interaction and CO2 emissions in the home micro-grid context are modelled and concurrently optimised for the first time while providing frequency regulation. The results from three case studies show that the proposed method reduces the energy cost, battery degradation, CO2 emissions and grid utilisation by 88.2%, 67%, 34% and 90% respectively, when compared to uncontrolled electric vehicle charging. Furthermore, with multiple optimal solutions, in order to achieve a 41.8% improvement in grid utilisation, the system operator needs to compensate the end electricity user and the electric vehicle owner for their incurred benefit loss of 27.34% and 9.7% respectively, to stimulate participation in energy services. Highlights  Optimisation of energy cost, battery degradation, grid utilisation and CO2 emission  The conflicts among objectives were addressed with multi-objective optimisation  A multi-criteria decision making process was tailored to the stakeholders

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Abstract-This paper provides an analysis of the experimental results available for lithium ion battery degradation which has been used to create a model of the effect of the identified parameters on the ageing of an EV battery. The parameters affecting degradation are generally accepted to be; state of charge, depth of discharge, charging rate and battery temperature. Values for each of these parameters have been found for three versions of a typical daily cycling scenario; uncontrolled charging, delayed charging and V2G. A comparison is made of the expected overall degradation using four different charging rates and different charging patterns based on the model. A link is made between the charging patterns and the effect on the power flow at the transformer of a typical section of LV network using a ADMD profile.The analysis shows that delayed charging and V2G slow down the rate of battery degradation. However, fast charging appears to accelerate battery degradation. Delayed charging also helps avoid excessive evening loading and thus will help delay distribution network asset upgrading. Uncontrolled charging increases evening loading and V2G can reduce it. However, the EV then needs more power for charging and the charging after V2G needs to be managed if it is not to create another spike in demand at a later time.Index Terms-Li ion battery, battery ageing, battery degradation, calendar life, cycle life, V2G

Spaces of urban disorder? Exposing the hidden nature and values of an English private urban allotment landscape.

No abstract

Developments in photovoltaic (PV) technologies and mass production have resulted in continuous reduction of PV systems cost. However, concerns remain about the financial feasibility for investments in PV systems, which is facing a global shrinking of government support. This work evaluates the investment attractiveness of rooftop PV installations and the impact of energy storage systems (ESS), using the UK as a case study. The evaluation considers the location of installation, the temporal evolution of the supporting policies, local energy consumption, electricity price and cost of investment at different years. Furthermore, the use of electric vehicles (EVs) as an alternative to ESS for complementing PV systems is also investigated. Optimization techniques are employed to schedule ESS and EV energy exchange in order to maximise the investment return. The results show that the net present value of PV systems in the UK has dropped from £28,650 in 2011 to £1,200 in 2017, due to declining government support towards PV technologies. It further shows that by incorporating ESS with PV systems, the benefit in 2017 can be increased by 46%. Conversely, employing the EV as energy storage would not bring additional benefits, considering the associated battery degradation and the current battery manufacturing cost.

Continuously changing electricity demand and intermittent renewable energy sources pose challenges to the operation of power systems. An alternative to reinforcing the grid infrastructure is to deploy and manage distributed energy storage systems. In this work, a micro-energy market is proposed for smart domestic energy trading in the low-voltage distribution systems in the context of high penetration of photovoltaic systems and battery energy storage systems. In addition, a micro-balancing market is proposed to address the congestions due to unforeseen energy imbalance.Centralised and decentralised management strategies are simulated in real time, based on generation and demand forecasts. In addition, electric vehicles are also simulated as potential storage solutions to improve grid operation. A techno-economic evaluation informs key stakeholders, in particular grid operators on strategies for a sustainable implementation of the proposed strategies. The results show that the micro-energy market reduces the energy cost for all grid users by 4.1-20.2%, depending on their configuration. In addition, voltage deviation, peak electricity demand and reverse power flow have been reduced by 12.8%, 7.7% and 85.6% respectively, with the proposed management strategies. The micro-balancing market has been demonstrated to keep the voltage profile and thermal characteristic within the set limit in case of contingency. KEY WORDS Micro energy market, Micro balancing market, Centralised and decentralised energy management, Real-time optimisation HIGHLIGHTS -Micro energy market reduced user's electricity cost -Micro balancing market solved the network contingency -Micro markets reduced voltage deviation, peak demand and reverse power flow -The system operator benefitted from the decentralised management of batteries -Decentralised management provided optimal grid operation and benefit of users Nomenclature ANN BAU Artificial neural network Business as usual BESS Battery energy storage system

Continuing in the footsteps of the three previous international conferences on Dynamics in Logistics, LDIC 2014 was the fourth event in this series to be held in Bremen (Germany) from February 10 to 14, 2014. The conference was accompanied by a "Doctoral Workshop" as well as the "InTraRegio International Dialog Event" and the "MAPDRIVER Kickoff Meeting" as satellite events. Similar to its predecessors LDIC 2007, LDIC 2009, and LDIC 2012, the Bremen Research Cluster for Dynamics in Logistics (LogDynamics) of the University of Bremen organized the conference in cooperation with the Bremer Institut für Produktion und Logistik (BIBA), which is a scientific research institute affiliated to the University of Bremen. The conference is concerned with the identification, analysis, and description of the dynamics of logistic processes and networks. The spectrum reaches from the modeling and planning of processes over innovative methods like autonomous control and knowledge management to the new technologies provided by radio frequency identification, mobile communication, and networking. The growing dynamic confronts the area of logistics with completely new challenges: it must become possible to rapidly and flexibly adapt logistic processes and networks to continuously changing conditions. LDIC 2014 provided a venue for researchers from academia and industry interested in the advances in dynamics in logistics induced by new technologies and methods. The conference addressed research in logistics from a wide range of fields including engineering, business administration, computer science, and mathematics. The LDIC 2014 proceedings consist of 72 papers including 10 young researcher papers selected by a strong reviewing process. The volume is organized into the following main areas: