Aoife Foley

State-of-the-art in electric vehicle charging infrastructure

The international introduction of electric vehicles (EVs) will see a change in private passenger car usage, operation and management. There are many stakeholders, but currently it appears that the automotive industry is focused on EV manufacture, governments and policy makers have highlighted the potential environmental and job creation opportunities while the electricity sector is preparing for an additional electrical load on the grid system. If the deployment of EVs is to be successful the introduction of international EV standards, universal charging hardware infrastructure, associated universal peripherals and user-friendly software on public and private property is necessary. The focus of this paper is to establish the state-of-the-art in EV charging infrastructure, which includes a review of existing and proposed international standards, best practice and guidelines under consideration or recommendation.

National climate policies across Europe and their impacts on cities strategies.

Globally, efforts are underway to reduce anthropogenic greenhouse gas emissions and to adapt to climate change impacts at the local level. However, there is a poor understanding of the relationship between city strategies on climate change mitigation and adaptation and the relevant policies at national and European level. This paper describes a comparative study and evaluation of cross-national policy. It reports the findings of studying the climate change strategies or plans from 200 European cities from Austria, Belgium, Estonia, Finland, France, Germany, Ireland, Italy, Netherlands, Spain and the United Kingdom. The study highlights the shared responsibility of global, European, national, regional and city policies. An interpretation and illustration of the influences from international and national networks and policy makers in stimulating the development of local strategies and actions is proposed. It was found that there is no archetypical way of planning for climate change, and multiple interests and motivations are inevitable. Our research warrants the need for a multi-scale approach to climate policy in the future, mainly ensuring sufficient capacity and resource to enable local authorities to plan and respond to their specific climate change agenda for maximising the management potentials for translating environmental challenges into opportunities.

Hybrid Probabilistic Wind Power Forecasting Using Temporally Local Gaussian Process

The demand for sustainable development has resulted in a rapid growth in wind power worldwide. Although various approaches have been proposed to improve the accuracy and to overcome the uncertainties associated with traditional methods, the stochastic and variable nature of wind still remains the most challenging issue in accurately forecasting wind power. This paper presents a hybrid deterministicprobabilistic method where a temporally local moving window technique is used in Gaussian process (GP) to examine estimated forecasting errors. This temporally local GP employs less measurement data with faster and better predictions of wind power from two wind farms, one in the USA and the other in Ireland. Statistical analysis on the results shows that the method can substantially reduce the forecasting error while it is more likely to generate Gaussian-distributed residuals, particularly for short-term forecast horizons due to its capability to handle the time-varying characteristics of wind power.

Electric Vehicles and energy storage — a case study on Ireland

Renewable energy is generally accepted as an important component of future electricity grids. In late 2008, the Government of the Republic of Ireland set a target of 10% of all vehicles in its transport fleet be powered by electricity by 2020. This paper examines the potential contributions Electric Vehicles (EVs) can make to facilitate increased electricity generation from variable renewable sources such as wind generation in the Republic of Ireland. It also presents an overview of the technical and economic issues associated with this target.

Wind power forecasting & prediction methods

Globally on-shore wind power has seen considerable growth in all grid systems. In the coming decade off-shore wind power is also expected to expand rapidly. Wind power is variable and intermittent over various time scales because it is weather dependent. Therefore wind power integration into traditional grids needs additional power system and electricity market planning and management for system balancing. This extra system balancing means that there is additional system costs associated with wind power assimilation. Wind power forecasting and prediction methods are used by system operators to plan unit commitment, scheduling and dispatch and by electricity traders and wind farm owners to maximize profit. Accurate wind power forecasting and prediction has numerous challenges. This paper presents a study of the existing and possible future methods used in wind power forecasting and prediction for both on-shore and off-shore wind farms.

Wind energy integration and the Ireland-Wales interconnector

In a small electricity grid such as the all Ireland system of Northern Ireland and the Republic of Ireland, managing with the variability of high levels of wind power generation will be crucial to ensuring the economic success of wind energy generation and the overall stability of the electricity system. Storage and interconnection are frequently proposed to manage this variability and total flexibility of the interconnector is assumed. This paper examines how market and meteorological effects could constrain interconnector operation. Currently an interconnector between the Republic of Ireland and Wales officially referred to as the East West Interconnector is in planning. The split between energy transfer and reserve provision through the interconnector will be dictated by energy prices and relative value of reserve services. The levels of wind power generation in each connected region may limit the mutual support expected from interconnection. In this regard wind forecasting and wind correlations in the connected regions are extremely relevant and are discussed in this paper.

Time series wind power forecasting based on variant Gaussian Process and TLBO

Due to the variability and stochastic nature of wind power, accurate wind power forecasting plays an important role in developing reliable and economic power system operation and control strategies. As wind variability is stochastic, Gaussian Process regression has recently been introduced to capture the randomness of wind energy. However, the disadvantages of Gaussian Process regression include its computation complexity and incapability to adapt to time varying time-series systems. A variant Gaussian Process for time series forecasting is introduced in this study to address these issues. This new method is shown to be capable of reducing computational complexity and increasing prediction accuracy. The convergence of the forecasting results is also proved. Further, a teaching learning based optimization (TLBO) method is used to train the model and to accelerate the learning rate. The proposed modelling and optimization method is applied to forecast both the wind power generation in Ireland and that from a single wind farm to demonstrate the effectiveness of the proposed method.

A new self-learning TLBO algorithm for RBF neural modelling of batteries in electric vehicles

One of the main purposes of building a battery model is for monitoring and control during battery charging/discharging as well as for estimating key factors of batteries such as the state of charge for electric vehicles. However, the model based on the electrochemical reactions within the batteries is highly complex and difficult to compute using conventional approaches. Radial basis function (RBF) neural networks have been widely used to model complex systems for estimation and control purpose, while the optimization of both the linear and non-linear parameters in the RBF model remains a key issue. A recently proposed meta-heuristic algorithm named Teaching-Learning-Based Optimization (TLBO) is free of presetting algorithm parameters and performs well in non-linear optimization. In this paper, a novel self-learning TLBO based RBF model is proposed for modelling electric vehicle batteries using RBF neural networks. The modelling approach has been applied to two battery testing data sets and compared with some other RBF based battery models, the training and validation results confirm the efficacy of the proposed method.

Optimal Scheduling Methods to Integrate Plug-In Electric Vehicles with the Power System: A Review

The introduction of the Tesla in 2008 has demonstrated to the public of the potential of electric vehicles in terms of reducing fuel consumption and green-house gas from the transport sector. It has brought electric vehicles back into the spotlight worldwide at a moment when fossil fuel prices were reaching unexpected high due to increased demand and strong economic growth. The energy storage capabilities from of fleets of electric vehicles as well as the potentially random discharging and charging offers challenges to the grid in terms of operation and control. Optimal scheduling strategies are key to integrating large numbers of electric vehicles and the smart grid. In this paper, state-of-the-art optimization methods are reviewed on scheduling strategies for the grid integration with electric vehicles. The paper starts with a concise introduction to analytical charging strategies, followed by a review of a number of classical numerical optimization methods, including linear programming, non-linear programming, dynamic programming as well as some other means such as queuing theory. Meta-heuristic techniques are then discussed to deal with the complex, high-dimensional and multi-objective scheduling problem associated with stochastic charging and discharging of electric vehicles. Finally, future research directions are suggested.

Electric vehicles and displaced gaseous emissions

Electric vehicles (EV) do not emit tailpipe exhaust fumes in the same manner as internal combustion engine vehicles. Optimal benefits can only be achieved, if EVS are deployed effectively, so that the tailpipe emissions are not substituted by additional emissions in the electricity sector. This paper examines the potential contributions that Plug in Hybrid Electric Vehicles can make in reducing carbon dioxide. The paper presents the results of the generation expansion model for Northern Ireland and the Republic of Ireland built using the dynamic programming based long term generation expansion planning tool called the Wien Automatic System Planning IV tool. The model optimizes power dispatch using hourly electricity demand curves for each year up to 2020, while incorporating generator characteristics and certain operational requirements such as energy not served and loss of load probability while satisfying constraints on environmental emissions, fuel availability and generator operational and maintenance costs. In order to simulate the effect of PHEV, two distinct charging scenarios are applied based on a peak tariff and an off peak tariff. The importance and influence of the charging regime on the amount of energy used and gaseous emissions displaced is determined and discussed.