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

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

No abstract

No abstract