Innovation, the diesel engine and vehicle markets: Evidence from OECD engine patents

This paper uses a patent data set to identify factors fostering innovation of diesel engines between 1974 and 2010 in the OECD region. The propensity of engine producers to innovate grew by 1.9 standard deviations after the expansion of the car market, by 0.7 standard deviations following a shift in the EU fuel economy standard, and by 0.23 standard deviations. The propensity to develop emissions control techniques was positively influenced by pollution control laws introduced in Japan, in the US, and in the EU, but not with the expansion of the car market. Furthermore, a decline in loan rates stimulated the propensity to develop emissions control techniques, which were simultaneously crowded out by increases in publicly-funded transport research and development. Innovation activities in engine efficiency are explained by market size, loan rates and by (Organisation for Economic Cooperation and Development) diesel prices, inclusive of taxes. Price effects on innovation, outweigh that of the US corporate average fuel economy standards. Innovation is also positively influenced by past transport research and development. © 2014 Elsevier Ltd

Energy Demand Reduction: supply chains and risk analysis

Data availability All data generated or analysed during this study are included in this published article.Copyright © The Author(s) 2023. Demand Reduction is a strategy with the potential to make a significant contribution to the energy supply/demand balance. Its two major themes are improving the energy efficiency of devices (appliances and processes) and changing people’s behaviour towards using less energy. In our analysis of a nation’s energy security, we treat Demand Reduction as an additional fuel which delivers ‘negafuel’, allowing a particular level of energy services to be met at a lower volume of supply than would be possible in its absence. In common with other fuels, negafuel is delivered by a supply chain with linked stages, all encountering risks of various types. A comprehensive survey of these risks in a case study of the UK shows that Demand Reduction belongs to a middle-ranking group of fuels in terms of overall risk. High-level risks encountered include the difficulty of assessing and delivering potential energy savings, the rate of building construction at the highest energy efficiency standards, optimism bias, changing policy and regulation, and operational failure (both of technology and policy). Assessing the risk of Demand Reduction as a supplied negafuel focuses attention on specific risks requiring mitigation, facilitating design of better policy, and more effective commercial products

A multi-agent model for assessing electricity tariffs

This paper describes the framework for modelling a multi-agent approach for assessing dynamic pricing of electricity and demand response. It combines and agent-based model with decision-making data, and a standard load-flow model. The multi-agent model described here represents a tool in investigating not only the relation between different dynamic tariffs and consumer load profiles, but also the change in behaviour and impact on low-voltage electricity distribution networks.The authors acknowledge the contribution of the EPSRC Transforming Energy Demand Through Digital Innovation Programme, grant agreement numbers EP/I000194/1 and EP/I000119/1, to the ADEPT project

Estimating the grid payments necessary to compensate additional costs to prospective electric vehicle owners who provide vehicle-to-grid ancillary services

The provision of ancillary services in the smart grid by electric vehicles is attractive to grid operators. Vehicles must be aggregated to meet the minimum power requirements of providing ancillary services to the grid. Likely aggregator revenues are insufficient to cover the additional battery degradation costs which would be borne by an existing electric vehicle owner. Moreover, aggregator revenues are insufficient to make electric vehicles competitive with conventional vehicles and encourage uptake by prospective consumers. Net annual costs and hourly compensation payments to electric vehicle owners were most sensitive to battery cost. The fleet provided firm fast reserve from 1900 h for 0.42 h, up to 2.7 h in the best cases. At best, likely aggregator revenue was 20 times less than the compensation required, up to 27,500 times at worst. The electric vehicle fleet may not be large enough to meet the firm fast reserve power and duration requirements until 2020. However, it may not be until 2030 that enough vehicles have been sold to provide this service cost-effectively. Even then, many more electric vehicles will be needed to meet the power level and duration requirements, both more often and for longer to enable participation in an all-day, everyday ancillary services market.The authors acknowledge the funding provided for this work by the Oxford Martin School

Analysing the rising price of new private housing in the UK: A national accounting approach

Copyright © 2022 The Authors. Discussion of the price of private new-build housing is dominated by land price, but is this the most important element? Other factors are examined for increasing prices, using the rich and robust datasets produced by government departments and agencies. In organising these complex datasets a Sankey diagram is introduced to explain the relationship between type of trade and type of work to show the relative importance of prices. The land value component has been trending downwards, so is not a factor in the rising prices of new private dwellings. Prices of components, other than land value, are obtained from gross fixed capital formation data and construction output. When corrected for inflation, these have risen by factors of 1.7 and 2.0, respectively, over 1998–2018. By including the self-employed, the total labour per new-build private dwelling is derived which has risen 2.4 to 3.0 man-years over 2011–2020. Since 2000, construction companies’ gross operating surplus per job has risen much faster than compensation of employees per job. This extra gross operating surplus, which can be associated with profit, totalled £11.6b in 2019 reaching £70k (at 2016 prices) per new private dwelling in 2019. Rising prices have created the opportunity for housebuilders to extract larger profits

Impacts of Raw Data Temporal Resolution Using Selected Clustering Methods on Residential Electricity Load Profiles

There is growing interest in discerning behaviors of electricity users in both the residential and commercial sectors. With the advent of high-resolution time-series power demand data through advanced metering, mining this data could be costly from the computational viewpoint. One of the popular techniques is clustering, but depending on the algorithm the resolution of the data can have an important influence on the resulting clusters. This paper shows how temporal resolution of power demand profiles affects the quality of the clustering process, the consistency of cluster membership (profiles exhibiting similar behavior), and the efficiency of the clustering process. This work uses both raw data from household consumption data and synthetic profiles. The motivation for this work is to improve the clustering of electricity load profiles to help distinguish user types for tariff design and switching, fault and fraud detection, demand-side management, and energy efficiency measures. The key criterion for mining very large data sets is how little information needs to be used to get a reliable result, while maintaining privacy and security

Investigation of boost pressure and spark timing on combustion and NO emissions under lean mixture operation in hydrogen engines

Hydrogen may become a replacement for liquid fossil fuels, contributing to greenhouse gas emissions reductions by improving the thermal efficiency of boosted lean burn spark ignition engines. Single-zone engine combustion models are simple, but can yield useful results as a step in the design process for developing alternative fuel systems. The single-zone thermodynamic model is advanced by implementing a laminar flame speed sub-model to investigate combustion, an extended Zeldovich mechanism for nitric oxide emissions, and incorporating the Livengood-Wu integral model for knock characteristics. The results were validated using published experiments giving satisfactory predictions between simulation and experiment for spark timing variation, manifold air pressure, and equivalence ratios. A detailed analysis of boosted lean burn strategies showed that nitric oxide emissions increased with boosted pressure, hence emissions can be controlled through optimizing the excess air ratio and the start of combustion. Further techniques to achieve high thermal efficiency and to prevent knock for boosted lean burn hydrogen SI engine are discussed

A stochastic model for estimating electric vehicle arrival at multi-charger forecourts

Data availability: Data will be made available on request.Copyright © 2022 The Author(s). Many countries are observing significant growth rates in electric vehicle (EV) uptake, often backed by financial incentives or regulation and legislation. The availability of large multi-charger sites for rapid EV charging with an experience similar to conventional refueling refuelling stations lowers the barrier to acceptance for drivers considering the switch to using an EV. The question arises about how to size such a facility at the design and planning stage, as well as accommodating growth in the number of EVs in daily use. One of the important factors is the vehicle arrival rate and the corresponding power and energy demand. EV charging is a function of several parameters, all of which are stochastic in nature, such as the vehicle daily travelled distance, charging start time and the required energy. To account for uncertainty in the parameters, a stochastic model has been designed to simulate realistic vehicle arrival rates. The model accounts for EVs coming from the site catchment area and opportunistic charging from passing traffic traveling travelling on the major roads adjacent to the site, the seasonality of parameters, and charging at places other than the site (competitive charging). The model produced plausible EV arrival patterns for both local and passing traffic, and reproduced the characteristic power demand at the case study site. All estimates incorporate uncertainty, reflecting realistic variability of the important parameters. The model in independent of location, uses open-source data, and is structured  flexibly, making it adaptable to new sites as part of the technical and business planning process.UK Research Council (UKRI) through Innovate UK under grant No. TS/T006471/1

The Skagit County choir COVID-19 outbreak – have we got it wrong?

Copyright © 2022 The Authors. Objectives: Over time, papers or reports may come to be taken for granted as evidence for some phenomenon. Researchers cite them without critically re-examining findings in the light of subsequent work. This can give rise to misleading or erroneous results and conclusions. We explore whether this has occurred in the widely reported outbreak of SARS-CoV-2 at a rehearsal of the Skagit Valley Chorale in March 2020, where it was assumed, and subsequently asserted uncritically, that the outbreak was due to a single infected person. Study design: Review of original report and subsequent modelling and interpretations. Methods: We reviewed and analysed original outbreak data in relation to published data on incubation period, subsequent modelling drawing on the data, and interpretations of transmission characteristics of this incident. Results: We show it is vanishingly unlikely that this was a single point source outbreak as has been widely claimed and on which modelling has been based. Conclusion: An unexamined assumption has led to erroneous policy conclusions about the risks of singing, and indoor spaces more generally, and the benefits of increased levels of ventilation. Although never publicly identified, one individual bears the moral burden of knowing what health outcomes have been attributed to their actions. We call for these claims to be re-examined and for greater ethical responsibility in the assumption of a point source in outbreak investigations.Funding: None declared

A reduced-dimension feature extraction method to represent retail store electricity profiles

Copyright © 2022 The Author(s). Characterising the inter-seasonal energy performance of buildings is a useful tool for a business to understand what is ‘normal’ for its portfolio of premises and to detect anomalous patterns of energy demand. When adding a new building to the portfolio, it will be useful to predict what will be the likely energy use as part of on-going monitoring of the site. For a large portfolio of buildings with, say, half-hourly energy use measurements (48 dimensions), analysis and prediction will require machine learning tools. Even so, it is advantageous to minimise the amount of data and number of dimensions and features required to find useful patterns in the measurement stream. Our aim is to devise a reduced feature set that can generate a statistically reasonable representation of daily electricity load profiles of retail stores and small supermarkets. We then test if our method is sufficiently accurate to predict and cluster measured patterns of demand. We propose an automatic method to extract features such as times and average demands from electricity load profiles. We used four regression models for prediction and six clustering methods to compare with the results obtained using all of the readings in the load profile. We found that the reduced feature set gave a good representation of the load profile, with only small prediction and clustering errors. The results are robust as prediction is supervised learning and clustering is unsupervised. This simplified feature set is a concise way to represent profiles without using small variances of the demand that do not add useful information to the overall picture. As modern sensor systems increase the volume, availability, and immediacy of data, using reduced dimensional datasets will be key to extracting useful information from high-resolution data streams