Mobile Personal Health Application for Empowering Diabetic Patients

In this paper we present the functional features of a mobile Personal Health Application that aims to empower Type 1 and Type 2 diabetic patients by facilitating self-management of their disease. The application supports the collection of observations of daily living i.e. vital signs, diet, quality and quantity of sleep, physical parameters such as weight, mental parameters such as self-assessment of quality of life, level of mood and stress, and physical activity related information. The application can operate in stand-alone mode as a consumer health app running in smartphones and tablets. However, the full range of its functionality is available when integrated with a server-based patient empowerment framework that further facilitates diabetes management with the active involvement of healthcare professionals, the exploitation of inclusive knowledge from clinical guidelines, and the incorporation of comprehensive information material

Developing novel technologies and services for intelligent low voltage electricity grids: cost–benefit analysis and policy implications

The paper presents a set of prototype smart grid technologies and services and validates the economic viability of the proposed solution using cost–benefit analysis (CBA). The study considered the EU-funded project called RESOLVD and implemented the technologies and services in a real-life pilot. the technologies and services on the EU-funded H2020. The paper focuses on the analysis of technological solutions which enhance the operational efficiency and the hosting capacity of low-voltage electricity distribution grids. The solutions provided better integration of a hybrid battery storage system, with the grid interfacing power electronics, smart gateways for the interconnection of assets at the grid edge, and sensors enhancing infrastructure observability and control. The result from the CBA indicates the economic viability of the project, high scalability, and replicability. The economic benefits were realized with the breakeven value of eight secondary substations (SS) and 16 feeders. The scenario test on the DSO’s willingness to pay for the software as a service (SaaS) revealed that the payback period can further be reduced by almost half with a higher internal rate of return (IRR) and net present value (NPV). Both the CBA and scenario tests showed RESOLVD solution can become more economically viable when deployed in largescale. Moreover, the CBA results provide evidence to the energy policy by allowing DSOs to consider both CAPEX and OPEX for better investment decisions. Further, the paper proposes an alternative business approach that shifts from grid reinforcement to service provision. The paper also discusses the research implications on energy policy and business.Peer ReviewedObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraObjectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats SosteniblesPostprint (published version

Leveraging bi-directional EV charging for flexibility services in the distribution grid - the case of fever project

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistributionDistribution System Operators face a challenging environment largely affected by the ever-growing integration of Distributed Energy Resources. Especially, Electric Vehicles have a rapidly growing presence in the distribution grids, being both a challenge, but also an enabler for active network management. This work analyses the operation of charging infrastructures in coordination with the DSO in the context of the FEVER project. The objective of the project is to exploit control of power flow through DC/AC converters towards demand management and voltage compensation. The paper describes the different modules required to coordinate this operation in a flexibility market context. DSO support tools have been developed to forecast possible critical events and prepare a mitigation plan leveraging flexibility. Response to this flexibility demand is covered by Vehicle-to-Grid charging stations, equipped with DC converters, capable of implementing flexibility strategies.Peer ReviewedObjectius de Desenvolupament Sostenible::13 - Acció per al ClimaObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantPostprint (published version

Particle Swarm Optimization in Residential Demand-Side Management: A Review on Scheduling and Control Algorithms for Demand Response Provision

Power distribution networks at the distribution level are becoming more complex in their behavior and more heavily stressed due to the growth of decentralized energy sources. Demand response (DR) programs can increase the level of flexibility on the demand side by discriminating the consumption patterns of end-users from their typical profiles in response to market signals. The exploitation of artificial intelligence (AI) methods in demand response applications has attracted increasing interest in recent years. Particle swarm optimization (PSO) is a computational intelligence (CI) method that belongs to the field of AI and is widely used for resource scheduling, mainly due to its relatively low complexity and computational requirements and its ability to identify near-optimal solutions in a reasonable timeframe. The aim of this work is to evaluate different PSO methods in the scheduling and control of different residential energy resources, such as smart appliances, electric vehicles (EVs), heating/cooling devices, and energy storage. This review contributes to a more holistic understanding of residential demand-side management when considering various methods, models, and applications. This work also aims to identify future research areas and possible solutions so that PSO can be widely deployed for scheduling and control of distributed energy resources in real-life DR applications