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Schwidtal, J. M., Piccini, P., Troncia, M., Chitchyan, R., Montakhabi, M., Francis, C., Gorbatcheva, A., Capper, T., Mustafa, M. A., Andoni, M., Robu, V., Bahloul, M., Scott, I. J., Mbavarira, T., España, J. M. E., & Kiesling, L. (2023). Emerging business models in local energy markets: a systematic review of peer-to-peer, community self-consumption, and transactive energy models. Renewable and Sustainable Energy Reviews, 179(June), 1-48. [113273]. https://doi.org/10.1016/j.rser.2023.113273 -- This publication is part of the work of the Global Observatory on Peer-to-Peer, Community Self-Consumption and Transactive Energy Models (GO-P2P), a task of the User-Centred Energy Systems Technology Collaboration Programme (Users TCP), under the auspices of the International Energy Agency. GO-P2P benefits from the support of Australia, Belgium, Ireland, Italy, the Netherlands, Switzerland, the United Kingdom and United States. GO-P2P is also supported by the EPSRC EnergyREV project EnergyREV (EP/S031863/1). The emergence of peer-to-peer, collective or community self-consumption, and transactive energy concepts gives rise to new configurations of business models for local energy trading among a variety of actors. Much attention has been paid in the academic literature to the transition of the underlying energy system with its macroeconomic market framework. However, fewer contributions focus on the microeconomic aspects of the broad set of involved actors. Even though specific case studies highlight single business models, a comprehensive analysis of emerging business models for the entire set of actors is missing. Following this research gap, this paper conducts a systematic literature review of 135 peer-reviewed journal articles to examine business models of actors operating in local energy markets. From 221 businesses in the reviewed literature, nine macro-actor categories are identified. For each type of market actor, a business model archetype is determined and characterised using the business model canvas. The key elements of each business model archetype are discussed, and areas are highlighted where further research is needed. Finally, this paper outlines the differences of business models for their presence in the three local energy market models. Focusing on the identified customers and partner relationships, this study highlights the key actors per market model and the character of the interactions between market participants. preprint authorsversion published

This paper proposes an efficient and dynamic process to evaluate the smart grid research and innovation (R&I) activities in the EU Member States (MS) at the national level where the R&I status of smart grid technologies is identified by analysing the outcomes of completed projects and R&I priorities are extracted from the national energy and climate plan (NECP). Throughout the process, interaction with stakeholders is also very important to identify the R&I needs, verify the outcomes and hence be included in the process. The evaluation outcomes, including the identified technological gaps, can be exploited further to strengthen the action plan of decarbonising the smart grid network at the national level. The proposed process can also be implemented at a regional level to achieve the regional targets. This paper validates the proposed approach by presenting the findings at national and regional levels of three Member States (MS)

Distributed energy generations (DEGs) form one of the main pillars enabling DER at the citizens and residential premises. Effective deployment of these resources has a key role in the energy transition's success. Compared to the other energy sector stakeholders, citizens have many specificities that need to be addressed. Analysing and meeting the needs and securing potential engagement in the energy transition is more complicated than dealing with organisations, companies or institutions. This paper thus very specifically analyse the DEG citizen empowerment barriers and main enablers. It examines main challenges and bottlenecks, identifies key enablers, reviews good practices and suggests actions to achieve a smooth and efficient energy transition.

Capper, T., Gorbatcheva, A., Mustafa, M. A., Bahloul, M., Schwidtal, J. M., Chitchyan, R., Andoni, M., Robu, V., Montakhabi, M., Scott, I. J., Francis, C., Mbavarira, T., Espana, J. M., & Kiesling, L. (2022). Peer-to-peer, community self-consumption, and transactive energy: A systematic literature review of local energy market models. Renewable and Sustainable Energy Reviews, 162(July), 1-24. [112403]. https://doi.org/10.1016/j.rser.2022.112403 Peer-to-peer, community or collective self-consumption, and transactive energy markets offer new models for trading energy locally. Over the past five years, there has been significant growth in the amount of academic literature examining how these local energy markets might function. This systematic literature review of 139 peer-reviewed journal articles examines the market designs used in these energy trading models. A modified version of the Business Ecosystem Architecture Modelling framework is used to extract market model information from the literature, and to identify differences and similarities between the models. This paper examines how peer-to-peer, community self-consumption and transactive energy markets are described in current literature. It explores the similarities and differences between these markets in terms of participation, governance structure, topology, and design. This paper systematises peer-to-peer, community self-consumption and transactive energy market designs, identifying six archetypes. Finally, it identifies five evidence gaps which require future research before these markets could be widely adopted. These evidence gaps are the lack of: consideration of physical constraints; a holistic approach to market design and operation; consideration about how these market designs will scale; consideration of information security; and, consideration of market participant privacy. publishersversion published

Cardiovascular diseases (CVDs) remain the leading cause of death worldwide. Patients at risk of evolving CVDs are assessed by evaluating a risk factor-based score that incorporates different bio-markers ranging from age and sex to arterial stiffness (AS). AS depicts the rigidity of the arterial vessels and leads to an increase in the arterial pulse pressure, affecting the heart and vascular physiology. These facts have encouraged researchers to propose surrogate markers of cardiovascular risks and develop simple and non-invasive models to better understand cardiovascular system operations. This work thus fundamentally capitalizes on developing a novel class of low-dimensional physics-based fractional-order models of systemic arteries and exploring the feasibility of fractional differentiation order to portray the vascular stiffness. Fractional-order modeling is a successful paradigm to integrate multiscale and interconnected mechanisms of the complex arterial system. However, this type of modeling alone often fails to efficiently integrate altered variabilities in vascular physiology from various sources of large datasets, multi-modalities, and levels. In this regard, combining fractional-order-based approaches with machine learning techniques presents a unique opportunity to develop a powerful prediction framework that reveals the correlation between intertwined vascular events. This work is divided into three parts. The first part contributes to developing the fractional-order lumped parametric model of the arterial system. First, we propose fractional-order representations to model and characterize the complex and frequency-dependent apparent arterial compliance. Second, we propose fractional-order arterial Windkessel modeling the aortic input impedance and hemodynamic. Subsequently, the proposed models have been applied and validated using both human in-silico healthy datasets and real vascular aging and hypertension. The second part addresses the non-zero initial value problem for fractional differential equations (FDEs) and proposes an estimation technique for joint estimation of the input, parameters, and fractional differentiation order of non-commensurate FDEs. The performance of the proposed estimation techniques is illustrated on arterial and neurovascular hemodynamic response models. The third part explores the feasibility of using machine learning algorithms to estimate the gold-standard measurement of AS, carotid-to-femoral pulse wave velocity. Different modalities have been investigated to generate informative input features and reduce the dimensionality of the time series pulse waves.

Capper, T., Gorbatcheva, A., Mustafa, M. A., Bahloul, M., Schwidtal, J. M., Chitchyan, R., Andoni, M., Robu, V., Montakhabi, M., Scott, I., Francis, C., Mbavarira, T., Espana, J. M., & Kiesling, L. (2021). A Systematic Literature Review of Peer-to-Peer, Community Self-Consumption, and Transactive Energy Market Models. Social Science Research Network (SSRN), Elsevier. https://doi.org/10.2139/ssrn.3959620 Peer-to-peer and transactive energy markets, and community or collective self-consumption offer new models for trading energy locally. Over the past 10 years there has been significant growth in the amount of academic literature and trial projects examining how these energy trading models might function. This systematic literature review of 139 peer-reviewed journal articles examines the market designs used in these energy trading models. The Business Ecosystem Architecture Modelling framework is used to extract information about the market models used in the literature and identify differences and similarities between the models. This paper identifies six archetypal market designs and three archetypal auction mechanisms used in markets presented in the reviewed literature. It classifies the types of commodities being traded, the benefits of the markets and other features such as the types of grid models. Finally, this paper identifies five evidence gaps which need future research before these markets can be widely adopted. publishersversion published