Controlling the frequency of power systems with high wind power penetration is more difficult due to the high variability of the wind power. One possible mainstream energy carrier in the future, particularly for the transportation sector, is Hydrogen, and water electrolysis is one of the most attractive ways to produce it. In this study, a detailed model of a steam turbine generator has been produced in MATLAB Simulink and used to investigate a scenario in which there is a 25% penetration of wind power. To improve the frequency stability of the power system, large scale alkaline electrolysers used in future Hydrogen filling stations could adjust their load with respect to the frequency deviation from nominal and can significantly reduce fluctuations in system frequency. For the case examined, five times less spinning reserve is required in order to maintain the power system frequency within operational limits when electrolysers are utilised as a form of demand side management (DSM), compared to the base case where no electrolyser DSM plant is available. Actual operational data from a pressurised alkaline electrolyser is used to evidence the fast load changing capability of such electrolysers.

Second-life batteries are defined as those removed from electric vehicles (EVs) when their energy density and power density has degraded below the level required for motive applications but are still performant enough for less demanding stationary applications. They could one day be a plentiful, environmentally benign source of low-cost energy storage. Their price evolution is important to know for designers of and investors in such systems. A methodology is developed for predicting second-life battery price and sales quantities up to 2050. Although existing data is too scant to draw reliable quantitative conclusions, sensitivity analyses are run to investigate the effects of different EV uptake scenarios, new battery costs, refurbishment costs, recycling net credit, elasticity of supply, and size of demand. No previous work has incorporated all these driving factors in such a transparent way. The second-life price is found to be insensitive to most of these factors, while the quantity sold is sensitive to nearly all of them. Much work remains to be done in parameterizing the model more accurately. However, this work already elucidates a novel quantitative mode of thinking about what factors influence the long-term price and market size of second-life batteries.

The storage capacity of the batteries in an electric vehicle (EV) could be utilised to store electrical energy and give it back to the grid when needed by participating in vehicle to grid (V2G) schemes. This participation could be a source of revenue for vehicle owners thus reducing the total charging cost of their EVs. A V2G simulator has been developed using MATLAB to find out the potential cost saving from participation of EVs in V2G schemes. A standard IEEE30 network has been modelled in the simulator which uses the MATPOWER engine to undertake power flow analysis. A novel control algorithm has been developed to take advantage of the difference between the selling and buying electricity prices by charging and discharging EVs at the appropriate time. Two scenarios are simulated to compare the total charging cost of EVs with or without the utilisation of V2G technology within the power system assuming a total of 5000 EVs. The results of the simulation show that the applied control strategy with V2G is able to reduce the charging cost of EVs by 13.6 % while satisfying the minimum requirement for state of charge (SoC) of the EV batteries to complete their next journey.

High rate (<100kW) electric vehicle chargers (HREVCs) are crucial for achieving the benefits of reduced CO 2 and particulate emissions promised by electric vehicles by enabling journey distances greater than the range of the vehicle. A method for predicting the expected demand pattern at these HREVCs is presented in this paper. This is critical to planning a network of chargers. This novel method uses freely available traffic flow data and travel patterns extracted from the open street map combined with a novel EV battery capacity prediction method, to find future HREVC usage patterns in the UK and their dependence on location and EV characteristics. This planning method can be replicated to find HREVC power demand for any location on the strategic road network in the UK and can be used in analysis of the role of high rate EV charging in the wider energy system. Index Terms Vehicles, Battery chargers, Power system modeling, Load modeling I. INTRODUCTION The use of fossil fuel vehicles contributes to climate change through the release of CO 2 , nitrous oxides and unburnt hydrocarbons, and causes harmful levels of pollution to be present in cities around the world [1]. The removal of fossil fuels from the transport industry is therefore of clear importance. One method to achieve this which is currently gaining traction is the introduction of electric vehicles (EVs). EVs have the advantage of zero local gaseous emissions and improved

Hydrogen could become a significant fuel in the future especially within the transportation sector. Alkaline electrolysers supplied with power from renewable energy sources could be utilised to provide carbon free hydrogen for future hydrogen filling stations supplying Hydrogen Fuel Cell Vehicles (HFCV), or Internal Combustion Engines (ICEs) modified to burn hydrogen. However, there is a need to develop and use appropriate strategies such that the technology delivers greater economic and environmental benefits.