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This paper proposes a shared multi-stakeholder PV system for traction substations and nearby residential loads to reduce the need for storage, AC grid exchange, and curtailment. The residential stakeholders offer both the base electrical load and the solar panels installation space needed by the traction stakeholder, who brings the peak load and investments to the former. Two case studies were conducted for one year in the city of Arnhem, The cy=Netherlands, using comprehensive and verified simulation models: A high-traffic and a low-traffic substation. The results showed a positive, synergetic benefit in reducing the PV system's excess energy and size requirement for any type of traction substations connected to any number of households. In one detailed example, the multi-stakeholder system suggested in this paper is shown to reduce curtailment by up to 80% in moments of zero-traction load. Generally, the direct load coverage of a PV system is increased by as much as 7 absolute percentage points to the single-stakeholder system when looking at energy-neutral system sizes. This multi-stakeholders system offers then an increase in the techno-economic feasibility of PV system integration in urban loads. ; DC systems, Energy conversion & Storage

The future of road transportation systems faces fundamental changes concerning technological progress and business models. Automated and electric vehicles are coming into the market and evolving towards a service-based mobility system with promises to tackle energy and environmental issues in the mobility sector. Although recent studies have begun to explore the potential impact of shared and privately owned automated and electric vehicles (AEVs) mostly from an operational perspective, little is known about the life cycle impact of such future transport systems. To fill this gap, this paper aims to compare the life cycle environmental impacts of shared vs privately owned AEVs in a regional context. A life cycle assessment (LCA) approach is developed to appraise impact categories with a direct effect on human health, ecosystems, and resources availability. Given that automated vehicles are not yet being used massively, the LCA is applied to synthetic travel demand data to assess the characteristics of privately-owned AEVs and the results of an optimization model that determines the vehicle fleet and driving patterns of shared AEVs serving a regional case-study in the central region of Portugal. Two different vehicle seating capacities - one passenger (non-ridesharing) and four passengers (ridesharing) – are considered to evaluate shared mobility systems. Results show that shared mobility systems yield a potential reduction of up to 42% (with 4 passengers per vehicle) of the system's environmental impacts compared to privately owned automated vehicles. Human toxicity, mineral resource scarcity, and marine and freshwater ecotoxicity are the impact categories with a higher potential of reduction. ; Transport and Planning

This paper proposes an approach to capturing and reviewing scientific literature addressing latent topics across multiple scientific fields. As latent topics like moral values are affected by word polysemy and synonymy, a traditional keyword-based approach is often ineffective and therefore inappropriate. As a result, scientific literature addressing latent topics tends to be fragmented thereby constraining efforts to address similar and complementary research challenges. A novel approach to reviewing the literature by utilizing both semantic fields and probabilistic topic models has therefore been developed. We illustrate this approach by reviewing the literature addressing the value justice in the energy sector and compare this with a regular keyword-based approach. The new approach results in a more complete overview of the relevance of energy justice as compared to the traditional keyword-based approach. This novel approach can be applied to other latent topics including other values or phenomena such as societal resistance to technologies, thereby leading to an increased understanding of existing relevant literature and the identification of new areas of research.

Cost-optimal electricity system configurations with increasing renewable energy penetration were determined in this article for six islands of different geographies, sizes and contexts, utilizing photovoltaic energy, wind energy, pumped hydro storage and battery storage. The results of the optimizations showed strong reasoning for islands to invest in renewable energy technologies (particularly wind energy), as compared to conventional power generation. Levelized cost of systems for electricity generation decrease considerably with increasing renewable energy penetrations, to an optimal point in the range of 40–80% penetration. Furthermore, renewable electricity integration in the order of 60–90% could still be achieved with no added cost from the initial situation. Cost increases after these optimal points are attributed to the growing inclusion of storage, required to meet the higher renewable energy shares. However, with battery costs forecast to fall in the coming years, and a cost reduction of 50–70% already causing lithium-ion batteries to overtake pumped hydro as a cost-favorable storage option in this model, there is a real case for islands to begin their transition in a staged process; first installing wind and PV generation, and then - as storage costs decrease and their renewable energy capacities increase - investing in storage options.

The oil and gas industry is responsible for 6% of total global CO2 emissions, from exploration to downstream petrochemical production and account for another 50% when including the use of its products. Thus, this industry has a significant role in realising the target of net “zero” CO2 emissions by 2070, essential to limit global warming to 1.8 °C [2], as introduced under the Paris agreement. Currently, the interactions of an extensive set of individual and combined CO2 mitigation measures along the value chain and over time are poorly assessed. This paper aims to assess a bottom-up CO2 mitigation potential for a complex refinery, including portfolios of combined mitigation options, considering synergies, overlap, and interactions over time for more realistic insight into the costs and constraints of the mitigation portfolio. A total of 40 measures were identified, covering a wide range of technologies such as energy efficiency measures (EEM), carbon capture and storage (CCS), bio-oil co-processing, blue and green hydrogen (BH2, GH2), green electricity import, and electrification of refining processes linked to the transition of the Colombian energy systems. Five deployment pathways were assessed to achieve different specific targets: 1-base case scenario, 2-less effort, 3-maximum CO2 avoidance, 4-INDC, and 5-measures below 200 €/t CO2. Two scenarios (3 and 5) gave the highest GHG emission reduction potentials of 106% and 98% of refining process emissions, respectively. Although significant, it represent only around 13% of the life-cycle emissions when including upstream and final-use emissions of the produced fuels. Bio-oil co-processing options account for around 60% of the mitigation options portfolio, followed by CCS (23%), green electricity (7%) and green H2 (6%). The devised methodological approach in this study can also be applied to assess other energy-intensive industrial complexes and shed light on the bias for estimating CO2 mitigation potentials, especially when combining different mitigation ...

Integrated Biomass Gasifier Solid Oxide Fuel Cell Systems represent an alternative to fossil fuel based power plants, and direct internal tar reforming allows achieving high efficiency and decreasing system complexity. However, at present, tar is removed or reformed externally since there is not yet general agreement on the fate of these compounds in the anode chamber, and no information is available on the combined effects of tar and other biosyngas contaminants. In this paper, we present the results of short-term experiments on the cross-influence of HCl and tar on Ni-GDC Solid Oxide Fuel Cell anode and on direct internal tar reforming. Initially, the cell was fed with humidified hydrogen and an increasing concentration of HCl (8, 42 and 82 ppmv) and toluene (2.5, 4.2 and 8.4 g/Nm3) separately. Successively, 8.4 g/Nm3 of toluene and an increasing concentration of HCl were fed to the cell. We used polarisation and power density curves, and outlet gas composition analysis to evaluate the contaminants effects. The presence of HCl and toluene caused only a marginal increase in the cell Area Specific Resistance (around 1.5% when the cell was operated at Open Circuit), and the Area Specific Resistance remained then constant during the exposure time. However, HCl affects tar reforming decreasing the concentration of CO2 and CO at the cell outlet. The results indicate the feasibility of direct internal toluene reforming and suggest the revision of currently used tolerance limits based on contaminants cross-influence effects. Extensive research on this topic is still required.

Motivated by the increasing integration among electricity markets, in this paper we propose two different methods to incorporate market integration in electricity price forecasting and to improve the predictive performance. First, we propose a deep neural network that considers features from connected markets to improve the predictive accuracy in a local market. To measure the importance of these features, we propose a novel feature selection algorithm that, by using Bayesian optimization and functional analysis of variance, evaluates the effect of the features on the algorithm performance. In addition, using market integration, we propose a second model that, by simultaneously predicting prices from two markets, improves the forecasting accuracy even further. As a case study, we consider the electricity market in Belgium and the improvements in forecasting accuracy when using various French electricity features. We show that the two proposed models lead to improvements that are statistically significant. Particularly, due to market integration, the predictive accuracy is improved from 15.7% to 12.5% sMAPE (symmetric mean absolute percentage error). In addition, we show that the proposed feature selection algorithm is able to perform a correct assessment, i.e. to discard the irrelevant features.

Urban areas consist of a mix of households and services, such as offices, shops, schools, etc. Yet most urban energy models only consider household load profiles, omitting the service sector. Realistic assessment of the potential for renewable resource integration in cities requires models that include detailed demand and generation profiles. Detailed generation profiles are available for many resources. Detailed demand profiles, however, are currently only available for households and not for the service sector. This paper addresses this gap. The paper (1) proposes a novel approach to devise synthetic service sector demand profiles based on a combination of a large number of different data sources, and (2) uses these profiles to study the impact of the service sector on the potential for renewable resource integration in urban energy systems, using the Netherlands as a case study. The importance of the service sector is addressed in a broad range of solar and wind generation scenarios, and in specific time and weather conditions (in a single scenario). Results show that including the service sector leads to statistically significantly better estimations of the potential of renewable resource integration in urban areas. In specific time and weather conditions, including the service sector results in estimations that are up to 33% higher than if only households are considered. The results can be used by researchers to improve urban energy systems models, and by decision-makers and practitioners for grid planning, operation and management}. 32 pages, 7 figures, 4 tables