Quantifying the increasing sensitivity of power systems to climate variability

Large quantities of weather-dependent renewable energy generation are expected in power systems under climate change mitigation policies, yet little attention has been given to the impact of long term climate variability. By combining state-ofthe-art multi-decadal meteorological records with a parsimonious representation of a power system, this study characterises the impact of year-to-year climate variability on multiple aspects of the power system of Great Britain (including coal, gas and nuclear generation), demonstrating why multi-decadal approaches are necessary. All aspects of the example system are impacted by inter-annual climate variability, with the impacts being most pronounced for baseload generation. The impacts of inter-annual climate variability increase in a 2025 wind-power scenario, with a 4-fold increase in the inter-annual range of operating hours for baseload such as nuclear. The impacts on peak load and peaking-plant are comparably small. Less than 10 years of power supply and demand data are shown to be insuffcient for providing robust power system planning guidance. This suggests renewable integration studies - widely used in policy, investment and system design - should adopt a more robust approach to climate characterisation

Privacy-enhancing Aggregation of Internet of Things Data via Sensors Grouping

Big data collection practices using Internet of Things (IoT) pervasive technologies are often privacy-intrusive and result in surveillance, profiling, and discriminatory actions over citizens that in turn undermine the participation of citizens to the development of sustainable smart cities. Nevertheless, real-time data analytics and aggregate information from IoT devices open up tremendous opportunities for managing smart city infrastructures. The privacy-enhancing aggregation of distributed sensor data, such as residential energy consumption or traffic information, is the research focus of this paper. Citizens have the option to choose their privacy level by reducing the quality of the shared data at a cost of a lower accuracy in data analytics services. A baseline scenario is considered in which IoT sensor data are shared directly with an untrustworthy central aggregator. A grouping mechanism is introduced that improves privacy by sharing data aggregated first at a group level compared as opposed to sharing data directly to the central aggregator. Group-level aggregation obfuscates sensor data of individuals, in a similar fashion as differential privacy and homomorphic encryption schemes, thus inference of privacy-sensitive information from single sensors becomes computationally harder compared to the baseline scenario. The proposed system is evaluated using real-world data from two smart city pilot projects. Privacy under grouping increases, while preserving the accuracy of the baseline scenario. Intra-group influences of privacy by one group member on the other ones are measured and fairness on privacy is found to be maximized between group members with similar privacy choices. Several grouping strategies are compared. Grouping by proximity of privacy choices provides the highest privacy gains. The implications of the strategy on the design of incentives mechanisms are discussed

Environmental and financial implications of ethanol as a bioethylene feedstock versus as a transportation fuel

Bulk chemicals production from biomass may compete with biofuels for low-cost and sustainable biomass sources. Understanding how alternative uses of biomass compare in terms of financial and environmental parameters is therefore necessary to help ensure that efficient uses of resources are encouraged by policy and undertaken by industry. In this paper, we compare the environmental and financial performance of using ethanol as a feedstock for bioethylene production or as a transport fuel in the US life cycle-based models are developed to isolate the relative impacts of these two ethanol uses and generate results that are applicable irrespective of ethanol production pathway. Ethanol use as a feedstock for bioethylene production or as a transport fuel leads to comparable greenhouse gas (GHG) emissions and fossil energy consumption reductions relative to their counterparts produced from fossil sources. By displacing gasoline use in vehicles, use of ethanol as a transport fuel is six times more effective in reducing petroleum energy use on a life cycle basis. In contrast, bioethylene predominately avoids consumption of natural gas. Considering 2013 US ethanol and ethylene market prices, our analysis shows that bioethylene is financially viable only if significant price premiums are realized over conventional ethylene, from 35% to 65% depending on the scale of bioethylene production considered (80 000 t yr−1 to 240 000 t yr−1). Ethanol use as a transportation fuel is therefore the preferred pathway considering financial,GHGemissions, and petroleum energy use metrics, although bioethylene production could have strategic value if demand-side limitations of ethanol transport fuel markets are reached

Cloud cover effect of clear-sky index distributions and differences between human and automatic cloud observations

The statistics of clear-sky index can be used to determine solar irradiance when the theoretical clear sky irradiance and the cloud cover are known. In this paper, observations of hourly clear-sky index for the years of 2010--2013 at 63 locations in the UK are analysed for over 1 million data hours. The aggregated distribution of clear-sky index is bimodal, with strong contributions from mostly-cloudy and mostly-clear hours, as well as a lower number of intermediate hours. The clear-sky index exhibits a distribution of values for each cloud cover bin, measured in eighths of the sky covered (oktas), and also depends on solar elevation angle. Cloud cover is measured either by a human observer or automatically with a cloud ceilometer. Irradiation (time-integrated irradiance) values corresponding to human observations of "cloudless" skies (0 oktas) tend to agree better with theoretical clear-sky values, which are calculated with a radiative transfer model, than irradiation values corresponding to automated observations of 0 oktas. It is apparent that the cloud ceilometers incorrectly categorise more non-cloudless hours as cloudless than human observers do. This leads to notable differences in the distributions of clear-sky index for each okta class, and between human and automated observations. Two probability density functions---the Burr (type III) for mostly-clear situations, and generalised gamma for mostly-cloudy situations---are suggested as analytical fits for each cloud coverage, observation type, and solar elevation angle bin. For human observations of overcast skies (8 oktas) where solar elevation angle exceeds 10°, there is no significant difference between the observed clear-sky indices and the generalised gamma distribution fits

Optimisation of integrated bioenergy and concentrated solar power supply chains in South Africa

Climate change and energy security are complex challenges whose solutions depend on multi-faceted interactions between different actors and socio-economic contexts. Energy innovation through integration of renewable energies in existing systems offers a partial solution, with high potential identified for bioenergy and solar energy. In South Africa there is potential to further integrate renewable energies to meet local demands and conditions. Various concentrated solar power (CSP) projects are in place, but there is still land available to generate electricity from the sun. In combination with sustainable biomass resources these can offer synergetic benefits in improving the power generation’s flexibility. While thermodynamic and thermo-economic modelling for hybrid CSP-Biomass technology have been proposed, energy modelling in the realm of supply chains and demand/supply dynamics has not been studied sufficiently. We present a spatially and temporally Mixed Integer Linear Programming (MILP) model, to optimize the choice and location of technologies in terms of economic cost while being characterised by realistic supply/demand constraints as well as spatially-explicit environmental constraints. The model is driven by electricity demand, resource availability and technology costs as it aspires to emulate key energy and sustainability issues. A case study in the South African province of Gauteng was implemented over 2015-2050 to highlight the potential and challenges for hybrid CSP-Biomass and integrated systems assessment and the applicability of the modelling approach. From the range of hybrid CSP-Biomass technologies considered, based on detailed techno-economic characteristics from the literature, the Biomass only EFGT plant is identified as the cost optimal. When distributed generation (DG) technologies, small-scale Solar PV and Wind Turbines were introduced to the model as a competing alternative, they were demonstrated to be more economically optimal (€65 mil against €85 mil with CSP-Biomass Industrial scale), driven by technology learning cost reductions, evidencing the case for DG technologies to gain momentum. Together these scenarios highlight the possible carbon savings from integrating multiple renewable energy technologies

Diesel Emission Control -- Sulfur Effects (DECSE) Program; Phase I Interim Date Report No. 3: Diesel Fuel Sulfur Effects on Particulate Matter Emissions

The Diesel Emission Control-Sulfur Effects (DECSE) is a joint government/industry program to determine the impact of diesel fuel sulfur levels on emission control systems whose use could lower emissions of nitrogen oxides (NO{sub x}) and particulate matter (PM) from on-highway trucks in the 2002--2004 model years. Phase 1 of the program was developed with the following objectives in mind: (1) evaluate the effects of varying the level of sulfur content in the fuel on the emission reduction performance of four emission control technologies; and (2) measure and compare the effects of up to 250 hours of aging on selected devices for multiple levels of fuel sulfur content. This interim report covers the effects of diesel fuel sulfur level on particulate matter emissions for four technologies

Diesel Emission Control -- Sulfur Effects (DECSE) Program; Phase I Interim Data Report No. 1

The Diesel Emission Control-Sulfur Effects (DECSE) is a joint government/industry program to determine the impact of diesel fuel sulfur levels on emission control systems whose use could lower emissions of nitrogen oxides (NO{sub x}) and particulate matter (PM) from on-highway trucks in the 2002--2004 model years. Phase 1 of the program was developed with the following objectives in mind: (1) evaluate the effects of varying the level of sulfur content in the fuel on the emission reduction performance of four emission control technologies; and (2) measure and compare the effects of up to 250 hours of aging on selected devices for multiple levels of fuel sulfur content. This interim data report summarizes results as of August, 1999, on the status of the test programs being conducted on three technologies: lean-NO{sub x} catalysts, diesel particulate filters and diesel oxidation catalysts