The impact of domestic plug-in hybrid electric vehicles on power distribution system loads

The market for Plug-in Hybrid Electric Vehicle (PHEVs) is expected to grow significantly over the next few years and a number of new products are soon to come onto the market, such as the Toyota Prius plug-in version, . The charging demand of wide-scale use of PHEVs may have a significant impact on domestic electricity loads and could risk of overloading the power system if appropriate charging strategies not applied to prevent this. A Monte Carlo Simulation (MCS) model of domestic PHEV use and availability has been developed based on probabilistic characterisations obtained from UKTUS and quantifies charging demand of PHEVs as a function of time of day. The MCS model has been developed in order to simulate the impact on the electricity distribution system. This article also discusses the potential for responsive battery charging load from PHEVs

Accessing offshore wind turbines for maintenance : calculating access probabilities, expected delays and the associated costs using a probabilistic approach

There are ambitious plans in place for the expansion of offshore wind-power capacity in the EU and elsewhere. However, the cost of energy from offshore wind is much higher than that from land-based generation and anything between 15% and 30% of this cost is attributable to the cost of operation and maintenance (O&M). For exposed UK round three sites these costs could be higher still. The stochastic nature of the occurrence of faults, down-times due to adverse weather and sea-state and the associated losses in energy production, as well as vessel and personnel costs, all add to the potential risk to the finance of an offshore wind farm project. There is a clear need to estimate these effects and the risks associated with them when planning and financing a wind-farm. Key to all such calculations are the restrictions on safe access for maintenance associated with vessels and access methods and the consequent delays caused by adverse sea-state and weather. A computational approach has been developed at University of Strathclyde, based on an event tree and closed-form probabilistic calculations, enabling very fast estimates to be made of offshore access probabilities and expected delays using a simple spreadsheet. Examples are presented for calculations of accessibility. Turbine availability and loss of energy production are calculated based on given turbine component reliability data together with an agreed maintenance scheme. Direct maintenance cost and revenue lost due to down-time can also be calculated with suitable data on the costs of personnel, components, and vessel hire as well as electricity unit and ROC prices, and examples are given. Sensitivities to some of the key parameters are also presented

Estimating the cost of offshore maintenance and the benefit from condition monitoring

The EU generally, and the UK, Belgium, Netherlands and Germany specifically, have ambitious plans for the large scale installation of offshore wind-power capacity. However, the cost of energy from offshore wind is much higher than that from land-based generation and a substantial portion of that cost, anything between 15% and 30%, may be due to the cost of O&M alone, largely driven by delays in access and repair caused by adverse weather and sea-state, high vessel costs, higher wage costs, and lost revenue from extended down-time. As part of a condition monitoring project commissioned and funded by the ETI (Energy Technologies Institute), the authors have developed a simple tool to estimate the cost of O&M and associated lost revenue, and also to estimate the potential for condition monitoring to allow operators to reduce those costs and the loss in revenue through better maintenance scheduling. The tool builds on earlier work conducted at Strathclyde and presented at EOW 2009 on estimating offshore access delays and turbine availability using a closed form probabilistic method based on an event tree, but without extensive time-domain or Monte Carlo simulation. It currently uses wind and wave data, reliability data and component cost data mainly available in the public domain. Repairs and replacements of subsystems have been classified into a small range of different repair severities, each having their specific requirements for vessels, plant, personnel and time. Expected delays can be calculated directly for each type of repair and the overall effects are summed. Condition monitoring and other maintenance strategies are assumed to change the allocation of a particular subsystem's faults between repair categories and thereby affect its overall impact on down-time and other costs.Calculations are carried out in a spreadsheet that updates instantly when any parameter is changed. The advantage of the approach developed is that it is possible to explore the impact of changing access thresholds, reliabilities or site parameters quickly and easily without having to run a long series of simulations for each new situation

Network power flow analysis for a high penetration of distributed generation

Increasing numbers of very small generators are being connected to electricity distribution systems around the world. Examples include photovoltaics (PV) and gas-fired domestic-scale combined heat and power (micro-CHP) systems, with electrical outputs in the region of 1 to 2 kW. These generators are normally installed within consumers' premises and connected to the domestic electricity supply network (230 V single-phase in Europe, 120 V in North America). There is a growing need to understand and quantify the technical impact that high penetrations of such generators may have on the operation of distribution systems. This paper presents an approach to analyzing this impact together with results indicating that considerable penetrations of micro-generation can be accommodated in a typical distribution system

A cyclo-stationary complex multichannel wiener filter for the prediction of wind speed and direction

This paper develops a linear predictor for application to wind speed and direction forecasting in time and across different sites. The wind speed and direction are modelled via the magnitude and phase of a complex-valued time-series. A multichannel adaptive filter is set to predict this signal, based on its past values and the spatio-temporal correlation between wind signals measured at numerous geographical locations. The time-varying nature of the underlying system and the annual cycle of seasons motivates the development of a cyclo-stationary Wiener filter, which is tested on hourly mean wind speed and direction data from 13 weather stations across the UK, and shown to provide an improvement over both stationary Wiener filtering and a recent auto-regressive approach

Maximising revenue for non-firm distributed wind generation with energy storage in an active management scheme

The connection of high penetrations of renewable generation such as wind to distribution networks requires new active management techniques. Curtailing distributed generation during periods of network congestion allows for a higher penetration of distributed wind to connect, however, it reduces the potential revenue from these wind turbines. Energy storage can be used to alleviate this and the store can also be used to carry out other tasks such as trading on an electricity spot market, a mode of operation known as arbitrage. The combination of available revenue streams is crucial in the financial viability of energy storage. This study presents a heuristic algorithm for the optimisation of revenue generated by an energy storage unit working with two revenue streams: generation-curtailment reduction and arbitrage. The algorithm is used to demonstrate the ability of storage to generate revenue and to reduce generation curtailment for two case study networks. Studies carried out include a single wind farm and multiple wind farms connected under a 'last-in-first-out' principle of access. The results clearly show that storage using both operating modes increases revenue over either mode individually. Moreover, energy storage is shown to be effective at reducing curtailment while increasing the utilisation of circuits linking the distribution and transmission networks. Finally, renewable subsidies are considered as a potential third revenue stream. It is interesting to note that under current market agreements such subsidies have the potential to perversely encourage the installation of inefficient storage technologies, because of increased losses facilitating greater "utilisation" of renewable generation

Power system frequency management challenges - a probabilistic approach to assessing wind farm potential for aiding system frequency stability

With the increasing wind penetration level in power systems, transmission system operators have become concerned about frequency stability. The inertia of variable speed wind turbines are decoupled by power electronic converters from the power network and therefore do not intrinsically contribute to power system inertia. Besides, as wind plant displace conventional generation and their inertia, substantial reduction in power system inertia may occur. Variable speed wind turbines can be controlled to provide synthetic inertial response but have no direct contributions to power system inertia levels. A probabilistic approach to assessing wind farm potential for aiding frequency stability in power systems is proposed, and the method will be applied to the GB power system. The impact of the aggregate inertial response on arresting frequency fall is examined assuming a sudden generation loss of 1800 MW in the GB power system. The results show that inertial response from wind can reduce the maximum rate of fall of frequency and the minimum system frequency following the event (frequency nadir)

Literature Review : Electrical Energy Storage for Scotland

This report examines the role and value of energy storage in the context of electricity systems that are expected to absorb increasing quantities of time variable electricity generation from renewable sources in the years ahead. Particular attention is given to Scotland with its vast renewable energy potential and limited interconnection to the parts of the UK with the major electricity loads. Energy storage technologies cover a wide range of levels of development from mature technologies like pumped hydro with over 50 years of operational experience, to technologies still under development such as flow batteries and hydrogen storage systems, and all of these are reviewed. All scales of possible application are considered here from whole power system support, through community power provision, down to individual households

Gaussian process operational curves for wind turbine condition monitoring

Due to the presence of an abundant resource, wind energy is one of the most promising renewable energy resources for power generation globally, and there is constant need to reduce operation and maintenance costs to make the wind industry more profitable. Unexpected failures of turbine components make operation and maintenance (O&M) expensive, and because of transport and availability issues, the O&M cost is much higher in offshore wind farms (typically 30% of the levelized cost). To overcome this, supervisory control and data acquisition (SCADA) based predictive condition monitoring can be applied to remotely identify early failures and limit downtime, boost production and decrease the cost of energy (COE). A Gaussian Process is a nonlinear, nonparametric machine learning approach which is widely used in modelling complex nonlinear systems. In this paper, a Gaussian Process algorithm is proposed to estimate operational curves based on key turbine critical variables which can be used as a reference model in order to identify critical wind turbine failures and improve power performance. Three operational curves, namely, the power curve, rotor speed curve and blade pitch angle curve, are constructed using the Gaussian Process approach for continuous monitoring of the performance of a wind turbine. These developed GP operational curves can be useful for recognizing failures that force the turbines to underperform and result in downtime. Historical 10-min SCADA data are used for the model training and validation

Detailed pseudo-static drive train modelling with generator short circuit

Drivetrain failures contribute significantly to wind turbine downtime. Although the root causes of these failures are not yet fully understood, transient events are regarded as an important contributory factor. Despite extensive drive train modelling, limited work has been carried out to assess the impact of a generator short circuit on the drivetrain. In most cases, a generator short circuit is classed as a failure in itself with minimal focus on the subsequent effects on the gearbox and other drivetrain components. This paper will look to analyse the loading on the drivetrain for a doubly fed induction generator (DFIG) short circuit event with turbine ride through using a combination of Simulink, Garrad Hassan’s Bladed and RomaxWind drive train modelling software