Abstract
In this paper, we consider a demand response (DR) aggregator responsible for participating in the wholesale electricity market on behalf of the end-users who participated in the DR programs. Thus, the DR aggregator can trade its acquired DR within the short-term electricity markets, i.e., the day-ahead and the balancing (real-time) markets. In the proposed framework, the electricity market prices are considered uncertain, and a robust optimization approach is applied to address the uncertainties to maximize the profit of the DR aggregator. A model for analyzing the impact of the energy storage system (ESS) unit on a DR aggregator's performance is developed to provide more flexibility for the consumers. The direct interactions of a DR aggregator with an ESS are neglected in many models. However, this consideration can lead to improvement in the flexibility of the aggregator and also increase the profit of the entity by trading energy in the short-term markets to charge the ESS during the low-price periods and discharge it to the market while the electricity market prices are high. Hence, it is assumed that the DR aggregator owns an ESS unit and can cover a percentage of its traded power through the ESS. An analysis of the impact of the ESS unit on the DR aggregator's performance is applied to study the most appropriate size of the ESS that can maximize the profit of the aggregator. In addition, renewable energy production is employed for end-users through the installation of rooftop photovoltaic (PV) panels. This demand-side renewable generation can provide more flexibility for the participants in DR programs. Various feasible case studies have been applied to demonstrate the model's effectiveness and usefulness, and conclusions are duly drawn. The numerical results indicate that having an ESS seems necessary when the decision-maker desires to protect its profit from the worst-case scenarios and reduces the negative effect of the uncertain parameter, i.e., the wholesale electricity market prices. Thus, it can be shown that having a greater capacity for the ESS has a significant and direct impact on increasing the profit of the aggregator even in the worst-case scenarios, where the profit rises 20 % when the budget of uncertainty in the robust optimization is equal to 12.

Abstract

Abstract
The short-circuit current is one of the most important security operational parameters. With the increased penetration of DERs, it is crucial to frequently and periodically monitor it, ideally every 24 hours and with high granularity (e.g., 30 minutes). This paper develops a short-circuit computation methodology to calculate the complete short-circuit current in the TSO/DSO interface nodes (extra high voltage/high voltage (EHV/HV) substations), which could be used for operational planning purposes, considering the active contributions to the short-circuit current originating from both transmission and distribution networks. A TSO-DSO coordination procedure is presented to obtain the day-ahead short-circuit currents forecast. Moreover, two real cases are provided as examples for validation of the demonstrated procedures. © The Institution of Engineering and Technology 2023.

Abstract
Topics discussed on social media platforms contain a disparate amount of information written in colloquial language, making it difficult to understand the narrative of the topic. In this paper, we take a step forward, towards the resolution of this problem by proposing a framework that performs the automatic extraction of narratives from a document, such as tweet posts. To this regard, we propose a methodology that extracts information from the texts through a pipeline of tasks, such as co-reference resolution and the extraction of entity relations. The result of this process is embedded into an annotation file to be used by subsequent operations, such as visualization schemas. We named this framework Tweet2Story and measured its effectiveness under an evaluation schema that involved three different aspects: (i) as an Open Information extraction (OpenIE) task, (ii) by comparing the narratives of manually annotated news articles linked to tweets about the same topic and (iii) by comparing their knowledge graphs, produced by the narratives, in a qualitative way. The results obtained show a high precision and a moderate recall, on par with other OpenIE state-of-the-art frameworks and confirm that the narratives can be extracted from small texts. Furthermore, we show that the narrative can be visualized in an easily understandable way.

Abstract
By 2050, and in the context of decarbonization and carbon neutrality, many companies worldwide are looking for low-carbon alternatives. Transport companies are probably the most challenging due to the continuing growth in global demand and the high dependency on fossil fuels. Some alternatives are emerging to replace conventional diesel vehicles and thus reduce greenhouse gas emissions and air pollutants. One of these alternatives is the adoption of compressed natural gas (CNG). In this paper, we provide a detailed study of the current emissions from the largest bus fleet company in the metropolitan area of Oporto. For this analysis, we used a top-down and a bottom-up methodology based on EMEP/EEA guidebook to compute the CO2 and air pollution (CO, NMVOC, PM2.5, and NOx) emissions from the fleet. Fuel consumption, energy consumption, vehicle slaughter, electric bus incorporation, and the investments made were taken into consideration in the analyses. From the case study, the overall reduction in CO2 emission was just 6.3%, and the emission factors (air pollutants) from CNG-powered buses and diesel-powered buses are closer and closer. For confirming these results and question the effectiveness of the fleet transitions from diesel to CNG vehicles, we analysed two scenarios. The obtained results reveal the potential and effectiveness of electric buses and other fuel alternatives to reduce CO2 and air pollution.

Abstract
This paper introduces a mathematical model designed to optimise the operation of natural gas distribution networks, considering the injection of hydrogen in multiple nodes. This proposal is devised to optimise the quantity of hydrogen injected to maintain pressure, gas flows, and gas quality indexes (Wobbe Index (WI) and the Higher Heating Value (HHV)) within admissible limits. The model has been applied to a gas network case study with three distinct scenarios and implemented using Python. The findings from the case study show the maximum permissible volume of hydrogen in the network, quantify the total savings in natural gas, and estimate the reduction in carbon dioxide emissions. © 2023 IEEE.