Exploring the role of reanalysis data in simulating regional wind generation variability over Northern Ireland

As wind generation increases, system impact studies rely on predictions of future generation and effective representation of wind variability. A well-established approach to investigate the impact of wind variability is to simulate generation using observations from 10 m meteorological mast-data. However, there are problems with relying purely on historical wind-speed records or generation histories: mast-data is often incomplete, not sited at a relevant wind generation sites, and recorded at the wrong altitude above ground (usually 10 m), each of which may distort the generation profile. A possible complimentary approach is to use reanalysis data, where data assimilation techniques are combined with state-of-the-art weather forecast models to produce complete gridded wind time-series over an area. Previous investigations of reanalysis datasets have placed an emphasis on comparing reanalysis to meteorological site records whereas this paper compares wind generation simulated using reanalysis data directly against historic wind generation records. Importantly, this comparison is conducted using raw reanalysis data (typical resolution ∼50 km), without relying on a computationally expensive “dynamical downscaling” for a particular target region. Although the raw reanalysis data cannot, by nature of its construction, represent the site-specific effects of sub-gridscale topography, it is nevertheless shown to be comparable to or better than the mast-based simulation in the region considered and it is therefore argued that raw reanalysis data may offer a number of significant advantages as a data source.

Improving EU biofuels policy? Greenhouse gas emissions, policy efficiency, and WTO compatibility

Both the EU’s Renewable Energy Directive (RED) and Article 7a of its Fuel Quality Directive (FQD) seek to reduce greenhouse gas (GHG) emissions from transport fuels. The RED mandates a 10% share of renewable energy in transport fuels by 2020, whilst the FQD requires a 6% reduction in GHG emissions (from a 2010 base) by the same date. In practice, it will mainly be biofuels that economic operators will use to meet these requirements, but the different approaches can lead to either the RED, or the FQD, acting as the binding constraint. A common set of environmental sustainability criteria apply to biofuels under both the RED and the FQD. In particular, biofuels have to demonstrate a 35% (later increasing to 50/60%) saving in life-cycle GHG emissions. This could be problematic in the World Trade Organization (WTO), as a non-compliant biofuel with a 34% emissions saving would probably be judged to be ‘like’ a compliant biofuel. A more economically rational way to reduce GHG emissions, and one that might attract greater public support, would be for the RED to reward emission reductions along the lines of the FQD. Moreover, this modification would probably make the provisions more acceptable in the WTO, as there would be a clearer link between policy measures and the objective of reductions in GHG emissions; and the combination of the revised RED and the FQD would lessen the commercial incentive to import biofuels with modest GHG emission savings, and thus reduce the risk of trade tension.

CASCADE: An Agent Based Framework For Modeling The Dynamics Of Smart Electricity Systems

The Complex Adaptive Systems, Cognitive Agents and Distributed Energy (CASCADE) project is developing a framework based on Agent Based Modelling (ABM). The CASCADE Framework can be used both to gain policy and industry relevant insights into the smart grid concept itself and as a platform to design and test distributed ICT solutions for smart grid based business entities. ABM is used to capture the behaviors of diff erent social, economic and technical actors, which may be defi ned at various levels of abstraction. It is applied to understanding their interactions and can be adapted to include learning processes and emergent patterns. CASCADE models ‘prosumer’ agents (i.e., producers and/or consumers of energy) and ‘aggregator’ agents (e.g., traders of energy in both wholesale and retail markets) at various scales, from large generators and Energy Service Companies down to individual people and devices. The CASCADE Framework is formed of three main subdivisions that link models of electricity supply and demand, the electricity market and power fl ow. It can also model the variability of renewable energy generation caused by the weather, which is an important issue for grid balancing and the profi tability of energy suppliers. The development of CASCADE has already yielded some interesting early fi ndings, demonstrating that it is possible for a mediating agent (aggregator) to achieve stable demandfl attening across groups of domestic households fi tted with smart energy control and communication devices, where direct wholesale price signals had previously been found to produce characteristic complex system instability. In another example, it has demonstrated how large changes in supply mix can be caused even by small changes in demand profi le. Ongoing and planned refi nements to the Framework will support investigation of demand response at various scales, the integration of the power sector with transport and heat sectors, novel technology adoption and diffusion work, evolution of new smart grid business models, and complex power grid engineering and market interactions.

Review of the potential for co-firing of cassava rhizome for generating heat and power in cassava based bio-ethanol plant in Thailand

Biomass is an important source of energy in Thailand and is currently the main renewable energy source, accounting for 40% of the renewable energy used. The Department of Alternative Energy and E�ciency (DEDE), Ministry of Thailand, has been promoting the use of renewable energy in Thailand for the past decade. The new target for renewable energy usage in the country is set at 25% of the �nal energy demand in 2021. Thailand is the world’s fourth largest producer of cassava and this results in the production of signi�cant amounts of cassava rhizome which is a waste product. Cassava rhizome has the potential to be co-�red with coal for the production of heat and power. With suitable co-�ring ratios, little modi�cation will be required in the co-�ring technology. This review article is concerned with an investigation of the feasibility of co-�ring cassava rhizome in a combined heat and power system for a cassava based bio-ethanol plant in Thailand. Enhanced use of cassava rhizome for heat and power production could potentially contribute to a reduction of greenhouse gas emissions and costs, and would help the country to meet the 2021 renewable energy target.

Assessing the performance of a building integrated BP c-Si PV system

In this study, the performance, yield and characteristics of a 16 year old photovoltaic (PV) system installation have been investigated. The technology, BP Saturn modules which were steel-blue polycrystalline silicon cells are no longer in production. A bespoke monitoring system has been designed to monitor the characteristics of 6 refurbished strings, of 18 modules connected in series. The total output of the system is configured to 6.5 kWp (series to parallel configuration). In addition to experimental results, the performance ratio (PR) of known values was simulated using PVSyst, a simulation software package. From calculations using experimental values, the PV system showed approximately 10% inferior power outputs to what would have been expected as standard test conditions. However, efficiency values in comparison to standard test conditions and the performance ratio (w75% from PVSyst simulations) over the past decade have remained practically the same. This output though very relevant to the possible performance and stability of aging cells, requires additional parametric studies to develop a more robust argument. The result presented in this paper is part of an on-going investigation into PV system aging effects.

Activities related with electricity consumption in the Spanish residential sector: variations between days of the week, Autonomous Communities and size of towns

Nowadays the electricity consumption in the residential sector attracts policy and research efforts, in order to propose saving strategies and to attain a better balance between production and consumption, by integrating renewable energy production and proposing suitable demand side management methods. To achieve these objectives it is essential to have real information about household electricity demand profiles in dwellings, highly correlated, among other aspects, with the active occupancy of the homes and to the personal activities carried out in homes by their occupants. Due to the limited information related to these aspects, in this paper, behavioral factors of the Spanish household residents, related to the electricity consumption, have been determined and analyzed, based on data from the Spanish Time Use Surveys, differentiating among the Autonomous Communities and the size of municipalities, or the type of days, weekdays or weekends. Activities involving a larger number of houses are those related to Personal Care, Food Preparation and Washing Dishes. The activity of greater realization at homes is Watching TV, which together with Using PC, results in a high energy demand in an aggregate level. Results obtained enable identify prospective targets for load control and for efficiency energy reduction recommendations to residential consumers.

An aggregated wind power generation model based on MERRA reanalysis data: MATLAB model and example data for the April 2014 wind farm distribution of Great Britain.

The MATLAB model is contained within the compressed folders (versions are available as .zip and .tgz). This model uses MERRA reanalysis data (>34 years available) to estimate the hourly aggregated wind power generation for a predefined (fixed) distribution of wind farms. A ready made example is included for the wind farm distribution of Great Britain, April 2014 ("CF.dat"). This consists of an hourly time series of GB-total capacity factor spanning the period 1980-2013 inclusive. Given the global nature of reanalysis data, the model can be applied to any specified distribution of wind farms in any region of the world. Users are, however, strongly advised to bear in mind the limitations of reanalysis data when using this model/data. This is discussed in our paper: Cannon, Brayshaw, Methven, Coker, Lenaghan. "Using reanalysis data to quantify extreme wind power generation statistics: a 33 year case study in Great Britain". Submitted to Renewable Energy in March, 2014. Additional information about the model is contained in the model code itself, in the accompanying ReadMe file, and on our website: http://www.met.reading.ac.uk/~energymet/data/Cannon2014/

Financial implications for companies running standby generators in support of a smart grid

Integrating renewable energy into built environments requires additional attention to the balancing of supply and demand due to their intermittent nature. Demand Side Response (DSR) has the potential to make money for organisations as well as support the System Operator as the generation mix changes. There is an opportunity to increase the use of existing technologies in order to manage demand. Company-owned standby generators are a rarely used resource; their maintenance schedule often accounts for a majority of their running hours. DSR encompasses a range of technologies and organisations; Sustainability First (2012) suggest that the System Operator (SO), energy supply companies, Distribution Network Operators (DNOs), Aggregators and Customers all stand to benefit from DSR. It is therefore important to consider impact of DSR measures to each of these stakeholders. This paper assesses the financial implications of organisations using existing standby generation equipment for DSR in order to avoid peak electricity charges. It concludes that under the current GB electricity pricing structure, there are several regions where running diesel generators at peak times is financially beneficial to organisations. Issues such as fuel costs, Carbon Reduction Commitment (CRC) charges, maintenance costs and electricity prices are discussed.

A field study of urban microclimates in London

This paper aims to address the characteristics of urban microclimates that affect the building energy performance and implementation of the renewable energy technologies. An experimental campaign was designed to investigate the microclimate parameters including air and surface temperature, direct and diffuse solar irradiation levels on both horizontal and vertical surfaces, wind speed and direction in a dense urban area in London. The outcomes of this research reveal that the climatic parameters are significantly influenced by the attributes of urban textures, which highlight the need for both providing the microclimatic information and using them in buildings design stages. This research provides a valuable set of microclimatic information for a dense urban area in London. According to the outcomes of this research, the feasibility study for implementation of renewable energy technologies and the thermal/ energy performance assessment of buildings need to be conducted using the microclimatic information rather than the meteorological weather data mostly collected from non-urban environments.

Using reanalysis data to quantify extreme wind power generation statistics : a 33 year case study in Great Britain

With a rapidly increasing fraction of electricity generation being sourced from wind, extreme wind power generation events such as prolonged periods of low (or high) generation and ramps in generation, are a growing concern for the efficient and secure operation of national power systems. As extreme events occur infrequently, long and reliable meteorological records are required to accurately estimate their characteristics. Recent publications have begun to investigate the use of global meteorological “reanalysis” data sets for power system applications, many of which focus on long-term average statistics such as monthly-mean generation. Here we demonstrate that reanalysis data can also be used to estimate the frequency of relatively short-lived extreme events (including ramping on sub-daily time scales). Verification against 328 surface observation stations across the United Kingdom suggests that near-surface wind variability over spatiotemporal scales greater than around 300 km and 6 h can be faithfully reproduced using reanalysis, with no need for costly dynamical downscaling. A case study is presented in which a state-of-the-art, 33 year reanalysis data set (MERRA, from NASA-GMAO), is used to construct an hourly time series of nationally-aggregated wind power generation in Great Britain (GB), assuming a fixed, modern distribution of wind farms. The resultant generation estimates are highly correlated with recorded data from National Grid in the recent period, both for instantaneous hourly values and for variability over time intervals greater than around 6 h. This 33 year time series is then used to quantify the frequency with which different extreme GB-wide wind power generation events occur, as well as their seasonal and inter-annual variability. Several novel insights into the nature of extreme wind power generation events are described, including (i) that the number of prolonged low or high generation events is well approximated by a Poission-like random process, and (ii) whilst in general there is large seasonal variability, the magnitude of the most extreme ramps is similar in both summer and winter. An up-to-date version of the GB case study data as well as the underlying model are freely available for download from our website: http://www.met.reading.ac.uk/~energymet/data/Cannon2014/.

The importance of accurate wind resource assessment for evaluating the economic viability of small wind turbines

The techno-economic performance of a small wind turbine is very sensitive to the available wind resource. However, due to financial and practical constraints installers rely on low resolution wind speed databases to assess a potential site. This study investigates whether the two site assessment tools currently used in the UK, NOABL or the Energy Saving Trust wind speed estimator, are accurate enough to estimate the techno-economic performance of a small wind turbine. Both the tools tend to overestimate the wind speed, with a mean error of 23% and 18% for the NOABL and Energy Saving Trust tool respectively. A techno-economic assessment of 33 small wind turbines at each site has shown that these errors can have a significant impact on the estimated load factor of an installation. Consequently, site/turbine combinations which are not economically viable can be predicted to be viable. Furthermore, both models tend to underestimate the wind resource at relatively high wind speed sites, this can lead to missed opportunities as economically viable turbine/site combinations are predicted to be non-viable. These results show that a better understanding of the local wind resource is a required to make small wind turbines a viable technology in the UK.

Investigating preferences for dynamic electricity tariffs: the effect of environmental and system benefit disclosure

Dynamic electricity pricing can produce efficiency gains in the electricity sector and help achieve energy policy goals such as increasing electric system reliability and supporting renewable energy deployment. Retail electric companies can offer dynamic pricing to residential electricity customers via smart meter-enabled tariffs that proxy the cost to procure electricity on the wholesale market. Current investments in the smart metering necessary to implement dynamic tariffs show policy makers’ resolve for enabling responsive demand and realizing its benefits. However, despite these benefits and the potential bill savings these tariffs can offer, adoption among residential customers remains at low levels. Using a choice experiment approach, this paper seeks to determine whether disclosing the environmental and system benefits of dynamic tariffs to residential customers can increase adoption. Although sampling and design issues preclude wide generalization, we found that our environmentally conscious respondents reduced their required discount to switch to dynamic tariffs around 10% in response to higher awareness of environmental and system benefits. The perception that shifting usage is easy to do also had a significant impact, indicating the potential importance of enabling technology. Perhaps the targeted communication strategy employed by this study is one way to increase adoption and achieve policy goals.

Assessing the benefits of domestic hot fill washing appliances

Washing machine and dishwasher appliance use accounts for approximately 10% of electricity demand in EU households. The majority of this demand is due to the operation of electric heating elements inside appliances. This paper investigates the potential benefits that can be realised by adding a hot fill connection to washing appliances, with respect to carbon emissions, demand side management and renewable energy integration. Initial laboratory testing of new hot and cold fill appliances has resulted in modifications to optimise hot fill intake, and a novel numerical model presents a method of characterising appliance electricity use in different configurations. In order to validate model findings and test the use of new hot fill appliances in situ, a pilot study has recorded appliances’ resource consumption at one-minute resolution in fourteen households. The addition of hot fill reduced the total dishwasher and washing machine electricity consumption by 38% and 67% respectively. Depending on how hot water is supplied to appliances it is estimated that hot fill use results in an annual household carbon saving of up to 147 kgCO2. Further to direct electricity reduction, hot fill appliances can offer a method of time shifting demand away from peak periods without inconveniencing occupants’ lifestyles.

Economic valuation of an offshore wind farm in Greece: the role of individual’s base-state influences and beliefs in the value formation process

The increased concern for the impacts of climate change on the environment, along with the growing industry of renewable energy sources, and especially wind power, has made the valuation of environmental services and goods of great significance. Offshore wind energy is being exploited exponentially and its importance for renewable energy generation is increasing. We apply a double-bound dichotomous Contingent Valuation Method analysis in order to both a) estimating the Willingness to Pay (WTP) of Greek residents for green electricity produced by offshore wind farm located between the islands of Tinos and Andros and b) identifying factors behind respondents’ WTP including individual’s behaviour toward environment and individual’s views on climate change and renewable energy. A total of 141 respondents participated in the questionnaire. Results show that the respondents are willing to pay on average 20€ every two months through their electricity bill in return for carbon-free electricity and water saving from the wind farm. Respondents’ environmental consciousness and their perception towards climate change and renewable energy have a positive effect on their WTP.