Coordinated utilisation of wind farm reactive power capability for system loss optimisation

Most wind farms currently being installed are based upon doubly fed induction generator (DFIG) or direct-drive synchronous generator (DDSG) technology. Given that one of the impacts of introducing distributed generation is an alteration of steady-state power flows and voltages, both technologies are capable of providing local voltage support. Wind farms may, therefore, be included in optimal power flow (OPF) calculations to minimise fuel cost and/or network losses. The IEEE 30-bus system is considered as a case study, comparing fixed-speed induction generator (FSIG) requirements with DFIG capability. Results are presented for a range of DFIG capability modes, at varying system load and wind farm penetration levels. A significant reduction in losses can be achieved by suitable co-ordination of DFIG reactive power import/export, operating within typical grid code specifications. It is shown that the dynamic variability of reactive power requirements is readily accommodated by the power system. Finally, implementation options for the scheme and incentivising strategies are considered

Power System Steady-State Analysis with Large-Scale Electric Vehicle Integration

It is projected that the electric vehicle will become a dominant method of transportation within future road infrastructure. Moreover, the electric vehicle is expected to form an additional role in power systems in terms of electrical storage and load balancing. This paper considers the latter role of the electric vehicle and its impact on the steady-state stability of power systems, particularly in the context of large-scale electric vehicle integration. The paper establishes a model framework which examines four major issues: electric vehicle capacity forecasting; optimization of an object function; electric vehicle station siting and sizing; and steady-state stability. A numerical study has been included which uses projected United Kingdom 2020 power system data with results which indicate that the electric vehicle capacity forecasting model proposed in this paper is effective to describe electric vehicle charging and discharging profiles. The proposed model is used to establish criteria for electric vehicle station siting and sizing and to determine steady-state stability using a real model of a small-scale city power system

Understanding the Social Context of Eating with Multimodal Smartphone Sensing: The Role of Country Diversity

Understanding the social context of eating is crucial for promoting healthy eating behaviors by providing timely interventions. Multimodal smartphone sensing data has the potential to provide valuable insights into eating behavior, particularly in mobile food diaries and mobile health applications. However, research on the social context of eating with smartphone sensor data is limited, despite extensive study in nutrition and behavioral science. Moreover, the impact of country differences on the social context of eating, as measured by multimodal phone sensor data and self-reports, remains under-explored. To address this research gap, we present a study using a smartphone sensing dataset from eight countries (China, Denmark, India, Italy, Mexico, Mongolia, Paraguay, and the UK). Our study focuses on a set of approximately 24K self-reports on eating events provided by 678 college students to investigate the country diversity that emerges from smartphone sensors during eating events for different social contexts (alone or with others). Our analysis revealed that while some smartphone usage features during eating events were similar across countries, others exhibited unique behaviors in each country. We further studied how user and country-specific factors impact social context inference by developing machine learning models with population-level (non-personalized) and hybrid (partially personalized) experimental setups. We showed that models based on the hybrid approach achieve AUC scores up to 0.75 with XGBoost models. These findings have implications for future research on mobile food diaries and mobile health sensing systems, emphasizing the importance of considering country differences in building and deploying machine learning models to minimize biases and improve generalization across different populations

Understanding Social Context from Smartphone Sensing: Generalization Across Countries and Daily Life Moments

Understanding and longitudinally tracking the social context of people help in understanding their behavior and mental well-being better. Hence, instead of burdensome questionnaires, some studies used passive smartphone sensors to infer social context with machine learning models. However, the few studies that have been done up to date have focused on unique, situated contexts (i.e., when eating or drinking) in one or two countries, hence limiting the understanding of the inference in terms of generalization to (i) everyday life occasions and (ii) different countries. In this paper, we used a novel, large-scale, and multimodal smartphone sensing dataset with over 216K self-reports collected from over 580 participants in five countries (Mongolia, Italy, Denmark, UK, Paraguay), first to understand whether social context inference (i.e., alone or not) is feasible with sensor data, and then, to know how behavioral and country-level diversity affects the inference. We found that (i) sensor features from modalities such as activity, location, app usage, Bluetooth, and WiFi could be informative of social context; (ii) partially personalized multi-country models (trained and tested with data from all countries) and country-specific models (trained and tested within countries) achieved similar accuracies in the range of 80%-90%; and (iii) models do not generalize well to unseen countries regardless of geographic similarity

Microgrid cost optimization: a case study on Abu Dhabi

This paper presents a microgrid cost optimization study specifically focused on the United Arab Emirates (UAE) based on the Genetic and Ant-Bee Colony algorithms. The main objective of the paper is to identify size and amount of power supply sources in Microgrids that result in minimum cost. Specific parameters pertaining to the UAE were employed within the new objective function and constraints. Two different scenarios were tested, and their results have been discussed. During this study, it was evident that solar-PV systems were the second most cost-effective way to reduce cost of microgrids preceded by micro-turbines

Inferring accurate bus trajectories from noisy estimated arrival time records

National Research Foundation (NRF) Singapore under its International Research Centres in Singapore Funding Initiativ

Microgrids of commercial buildings: strategies to manage mode transfer from grid connected to islanded mode

Microgrid systems located within commercial premises are becoming increasingly popular and their dynamic behavior is still uncharted territory in modern power networks. Improved understanding in design and operation is required for the electricity utility and building services design sectors. This paper evaluates the design requirements for a commercial building microgrid system to facilitate seamless mode transition considering an actual commercial building microgrid system. A dynamic simulation model of the proposed microgrid system is established (utilizing DIgSILENT Power Factory) to aid the development of planning and operational philosophy for the practical system. An economic operational criterion is developed for the microgrid to incorporate selective mode transition in different time intervals and demand scenarios. In addition, a multi-droop control strategy has been developed to mitigate voltage and frequency variations during mode transition. Different system conditions considering variability in load and generation are analyzed to examine the responses of associated microgrid network parameters (i.e., voltage and frequency) with the proposed mode transition strategy during planned and unplanned islanding conditions. It has been demonstrated that despite having a rigorous mode transition strategy, control of certain loads such as direct online (DOL) and variable-speed-drive (VSD) driven motor loads is vital for ensuring seamless mode-transition, in particular for unplanned islanding conditions