Aidan Duffy

Assessing the Economic Benefits of Compressed Air Energy Storage for Mitigating Wind Curtailment

Renewable energy generation in the All-Island of Ireland (AII) is set to increase by 2020 due to binding renewable energy targets. To achieve these targets, there will be periods of time when 75% of electricity will be generated mainly from onshore wind. Currently, the AII system can accommodate a 50% maximum permissible instantaneous level of wind generation. The system operators must make system-wide wind curtailment decisions to ensure that this level is not breached. Subsequently, the ability to limit wind curtailment using large-scale energy storage such as pumped hydroelectric energy storage and compressed air energy storage (CAES) is increasingly being scrutinized as a viable option. Thus, the aims of this paper are to estimate the level of wind curtailment on the 2020 AII system for various scenarios including with and without CAES, and assess and quantify the revenue loss due to wind curtailment using power systems simulation software PLEXOS.

Assessing the benefits of compressed air energy storage on the 2020 Irish power system

Power systems have evolved as countries implement energy policies focusing on energy efficiency and increased share of renewable energy sources (RES). At the forefront is non-dispatchable generation such as wind and solar. Traditionally power systems were designed for fully dispatchable generating plant. However, these powers systems are under additional pressure due to the variable operational characteristics of RES. Consequently, capital investments in grid reinforcement, interconnection, additional gas generators and smart grid initiatives have been proposed and implemented. Moreover, an increased interest in energy storage technologies has evolved due to their various economic and operational benefits to power systems. Current compressed air energy storage (CAES) plants have shown economic feasibility and reliability. Thus, the main focus of this paper is to investigate and compare two scenarios; one without CAES and a second with CAES as an additional generator in the 2020 Irish power system using power systems simulation software PLEXOS.

IEA Wind Task 26. Wind Technology, Cost, and Performance Trends in Denmark, Germany, Ireland, Norway, the European Union, and the United States: 2007–2012

This report builds from a similar previous analysis (Schwabe et al., 2011) exploring the differences in cost of wind energy in 2008 among countries participating in IEA Wind Task 26 at that time. The levelized cost of energy (LCOE) is a widely recognized metric for understanding how technology, capital investment, operations, and financing impact the life-cycle cost of building and operating a wind plant. Schwabe et al. (2011) apply a spreadsheet-based cash flow model developed by the Energy Research Centre of the Netherlands (ECN) to estimate LCOE. This model is a detailed, discounted cash flow model used to represent the various cost structures in each of the participating countries from the perspective of a financial investor in a domestic wind energy project. This model is used for the present analysis as well, and comparisons are made for those countries who contributed to both reports, Denmark, Germany, and the United States.

A parametric analysis of domestic electricity consumption patterns in Ireland

This paper reports findings from a study of electrical load profiles obtained from a survey of a representative cross section of approximately 4,000 Irish dwellings. Electricity demand was recorded at half-hourly intervals for each dwelling over a six month period from 1st July 2009 to 31st December 2009. Descriptive statistics are shown for each electrical parameter such as mean, maximum demand, load factor and time of use (ToU) of electricity consumption. The mean power demand and daily mean load factor of the sample was 0.512kW and 23.43% respectively for all dwellings over the monitoring period. A mean daily maximum demand of 2.5kW was recorded for the sample. The most frequent ToU for maximum and minimum electricity consumption over the monitoring period was 18:00–18:30 and 06:30–07:00 respectively.

The impact of power curve estimation on commercial wind power forecasts — An empirical analysis

An increasing number of utilities participating in the energy market require short term (i.e. up to 48 hours) power forecasts for renewable generation in order to optimize technical and financial performances. As a result, a large number of forecast providers now operate in the marketplace, each using different methods and offering a wide range of services. This paper assesses five different day-ahead wind power forecasts generated by various service providers currently operating in the market, and compares their performance against the state-of-the-art of short-term wind power forecasting. The work focuses on how power curve estimations can introduce systematic errors that affect overall forecast performance. The results of the study highlight the importance of: accurately modelling the wind speed-to-power output relationships at higher wind speeds; taking account of power curve trends when training models; and the need to incorporate long-term (months to years) power curve variability into the forecast updating process.

Solar photovoltaic water pumping for Multiple Use Systems (MUS) in Nepal

This paper investigates the performance of five solar photovoltaic (PV) Multiple Use Systems (MUS) used for water pumping. The solar MUSs provide water for drinking, cleaning and micro-irrigation for some of the poorest communities in Nepal. In the absence of data logging, the performance of each system is investigated based on a series of rules of thumb to determine the predicted, expected and estimated demand and supply of water to small rural communities. The systems are compared based on their technical and economic performance and how this relates to local environmental, physical and socio-economic characteristics at each location.

Incorporation of Life Cycle Models in determining Optimal Wind Energy Infrastructural Provision

The deployment of wind energy has grown rapidly over the last two decades with an average annual growth rate of more than 26% since 1990. During this period the development and innovation of wind turbines has resulted in continual growth in wind turbine size with output ranges of 10-15MW likely to be deployed by 2020. This increased output has a knockon effect on the growth of rotor diameters and tower heights. Wind turbine towers are required to become taller, stronger and stiffer in order to carry the increased weight and associated structural loading. Consequently, the dimensions of the tower crosssections must be increased which results in manufacturing and transportation difficulties as well as increased material costs. Thus, this paper focuses on the development of wind energy technology over the last two decades and the optimisation techniques cited in current literature. From this, a multi-objective optimisation problem is defined as maximising the structural performance of wind turbine towers while simultaneously reducing the life cycle costs and emissions associated with electricity generation from wind. A multi-objective optimisation model based on a harmony search algorithm is presented. This model is proposed to be developed further in order to determine a set of optimal combinations known as Pareto optimal solutions, which will allow a trade-off between the life cycle costs and emissions. Findings from the continuing research are envisaged to support the deployment of large scale wind turbines both onshore and offshore from structurally more promising, economically more competitive and environmentally greener towers.

Using Life Cycle Assessment to Compare Wind Energy Infrastructure

Given the need to significantly reduce greenhouse gas (GHG) emissions due to the production of electricity, countries worldwide are trying to develop and implement different energy saving strategies and technologies to mitigate global warming. A core part of achieving this is the development and implementation of renewable energy technologies such as wind. This has resulted in the development and innovation of wind turbines with output ranges of 10-15MW likely to be deployed by 2020. This increased output has a knock on effect on the growth of rotor diameters and tower heights requiring the wind turbine system to be assessed from an economic, environmental and structural performance viewpoint. This has led to the proposal of using concrete as an alternative to the current preference of steel for wind turbine towers due to a number of limiting issues. Thus, the main focus of this paper is to investigate and compare the life cycle emissions (LCE) of GHG of concrete relative to steel as a tower solution in order to identify a solution for both onshore and offshore facilities. The main findings indicated that the LCE for a wind turbine with a concrete tower range between 4-9% lower than its equivalent steel solution over a 40 year life cycle.