Flexible Service Research Articles

Coordination of dynamic tariff and scheduled reprofiling product for day-ahead congestion management of distribution networks

The increasing penetration of distributed energy resources in distribution networks poses new challenges to the secure and efficient operation of distribution networks. One major challenge is the network congestion caused by the non-coordinated operation of flexible demands, such as electrical vehicles and heat pumps. In general, there are two categories of market-based day-ahead congestion management methods for distribution networks: price-based method and incentive-based method. In order to use the synergy of the two types of methods and to resolve potential conflicts against regulations when one type of method is implemented solely, a coordination scheme of the two types of methods is proposed for efficient day-ahead congestion management. In the proposed coordination scheme, the dynamic tariff (DT) as a price signal is used to partly resolve congestion firstly, and the scheduled reprofiling product (SRP) as an incentive-based flexibility service product is used to deal with the remaining congestion. By employing the coordination scheme, the distribution system operator (DSO) holds the profit neutral position in terms of congestion management, denoting that the DSO does not have the congestion management cost or revenue. Based on the DT model and SRP-based model, the coordination problem is formulated as a two-level non-convex mixed-integer non-linear programming (MINLP) model that is transformed into a one-level MINLP model with linear constraints and is solved by a proposed particle swarm optimization-based solution strategy. The Roy Billinton Test System was used to conduct case studies to validate the effectiveness of the proposed coordination scheme for day-ahead congestion management in distribution networks. The case study results demonstrate that the proposed coordination scheme can efficiently resolve congestion by the coordination of the DTs and SRPs while ensuring that the DSO is in a profit neutral position.

Non-linear grey-box modelling for heat dynamics of buildings

This paper introduces a non-linear grey-box (GB) model based on stochastic differential equations that describes the heat dynamics of a school building in Denmark, equipped with a water-based heating system. The building is connected to a local district heating network through a heat exchanger. The heat is delivered to the rooms mainly through radiators and partially through a ventilation system. A monitoring system based on IoT sensors provides data on indoor climate in the rooms and on the heat load of the building. Using this data, we estimate unknown states and parameters of a model of the building’s heating system using the maximum likelihood method. Important novelties of this paper include models of the water flow in the circuit and the state of the valves in the radiator thermostats. The non-linear model accurately predicts the indoor air temperature, return water temperature and heat load. The ideas behind the model lay a foundation for GB models of buildings that use different kinds of water-based heating systems such as air-to-water/water-to-water heat pumps. Such GB models enable model predictive control to control e.g. the indoor air climate or provide flexibility services.

Evaluation of a battery energy storage system in hospitals for arbitrage and ancillary services

The ambitious target of reaching net-zero greenhouse gas emissions by 2050 in the UK, which includes the decarbonisation of heat and electricity, means the increase of instantaneous power from non-dispatchable renewable energy sources (RESs). The intermittency of RESs will cause stability issues for the grid resulting from the mismatch between generation from RES and load demand. Battery energy storage systems (BESS) can match loads with generation and can provide flexibility to the grid. This study is proposing the health sector as a new flexibility services provider for the grid through BESS. The health sector has large loads that run throughout the year, and by managing this load it can provide flexibility to the grid. Four different scenarios have been evaluated for a range of behind-the-meter (BTM) BESS for a hospital in the UK to provide arbitrage and ancillary services considering the option of installing a photovoltaic (PV) system. It was found that BESS would not be economically viable through arbitrage alone since the payback period was always greater than the BESS lifetime. However, bundling services by participating in the ancillary services market resulted in payback periods as low as 3.10 years for some systems, and the net present value (NPV) could reach more than £5 million. This work provides evidence that the health sector can be a significant player in the transition to a renewables-led energy system, an exemplar for other sectors, and one of the solutions to recovery from the COVID19 pandemic.

Advanced Flexibility Market for System Services Based on TSO–DSO Coordination and Usage of Distributed Resources

The high and growing share of renewable sources and the more important role of Distributed Energy Resources (DERs) in the Distribution System (DS) is leading to a need for more efficient coordination of those sources. In future power systems, TSO–DSO coordination will play a key role in providing flexibility services. Lack of proper coordination of sources may lead to congestion in the network or to a lack of possibility to generate or consume energy on a requested level. The crucial aspect is that the TSO–DSO coordination must be based on an active role of all participants: TSO, DSO, generation units and the demand. This paper presents the possible application of the TSO–DSO coordination by providing the flexibility services from DS to the Transmission System (TS). The paper presents the complex optimization of TS, DS and its coordination. The main goal of the paper is to show the possibility of the application of the flexibility market into the current system design. It requires the creation of a new platform, where the offers of the flexibility services could be submitted and then exchanged between entities. The paper shows that the usage of flexibility services may decrease the operational cost of the system, and the DERs providing those services may benefit from an additional source of income.

Primary Health Care and Hospital Management During COVID-19: Lessons from Lombardy.

The pandemic due to SARS-CoV-2 tested the resilience of health systems worldwide. The outcome of the pandemic is impacted by health management choices made over the course of the disaster, which in turn are strongly dependent on the underlying healthcare system – as mirrored by the fact that regional pandemic experiences differ considerably: In Italy (a country most impacted by the COVID-19 outbreak), infection and mortality rates vary vastly between regions, with Lombardy – a comparatively well-equipped region with regard to hospitals and centers of scientific excellence – being amongst the worst-affected areas. Within this article, we focus on the challenges within primary health care and hospital organization, cooperation between primary and specialist care, and access to health care services: In Lombardy, neglected primary health care with a comparatively low availability of general practitioners (GPs) per inhabitant, the initial prioritization of hospitals during the pandemic while neglecting primary health care in terms of personal protective equipment (PPE), the lack of testing resources, and a failure to achieve coordinated support contributed to a quick overburdening of hospitals, where the dissolution of traditional departments into “macro-areas” may favor nosocomial infections during an ongoing pandemic. Neither specialized medicine nor privatization, but rather flexible public healthcare services working in consistent cooperation with GPs, show better efficiency in containing viral spread and managing patients. Strengthening the primary health care sector with regard to human and technical resources and supporting the coordination between the different levels of health care providers help to avoid overcrowded hospitals, while protecting patients and health care workers during large-scale health emergencies. Overall, further in-depth analysis of structural determinants is needed in order to develop more-resilient and integrative health care systems.

Cost-minimized virtual elastic optical network provisioning with guaranteed QoS

Network virtualization is important for elastic optical networks (EONs) because of more flexible service provisioning. To ensure guaranteed quality of service (QoS) for each virtual elastic optical network (VEON), clients usually request network resources from a network operator based on their bandwidth requirements predicted from historical traffic demands. However, this may not be efficient as the actual traffic demands of users always fluctuate. To tackle this, we propose a new VEON service provisioning scheme, called SATP, which consists of three stages, i.e., spectrum assignment (SA), spectrum trading (ST), and spectrum purchasing (SP). Unlike conventional once-for-all VEON service provisioning approaches, the SATP scheme first allocates spectrum resources to VEONs according to their predicted bandwidth requirements with a satisfaction ratio α (0 < α < 1). Then, to minimize service degradation on VEONs which are short of assigned spectra for their peak traffic periods, the scheme allows VEONs to trade spectra with each other according to their actual bandwidth requirements. Finally, it allows VEON clients to purchase extra spectrum resources from a network operator if the spectrum resources are still insufficient. To optimize this entire process, we formulate the problem as a mixed integer linear programming (MILP) model and also develop efficient heuristic algorithms for each stage to handle large test scenarios. Simulations are conducted under different test conditions for both static and dynamic traffic demand scenarios. Results show that the proposed SATP scheme is efficient and can achieve significant performance improvement under both static and dynamic scenarios.

A Review on Security Aspects and Countermeasures for Cloud Computing

For large-scale companies or people that desire a range of system services at a cheap cost, cloud computing is now the most popular phenomena to use. Personal information is frequently kept in a public cloud that is open to the public. This fundamental raises a number of concerns about cloud providers' flexible services, including confidentiality, persistence, and endurance. The paper aims to better understand cloud components, security concerns, and dangers, as well as developing solutions that might help minimise cloud vulnerabilities. It is a well-known truth that the cloud has been a viable hosting platform since 2008; nevertheless, the view of cloud security is that it requires major changes in order to achieve higher rates of adaptability at the corporate scale. Many of the difficulties affecting cloud computing need to be rectified immediately. The industry has made tremendous progress in combating cloud computing risks, but there is still work to be done to reach the level of maturity that traditional/on-premise hosting has.

The Challenges of Low Voltage Distribution System State Estimation—An Application Oriented Review

Global trends such as the growing share of renewable energy sources in the generation mix, electrification, e-mobility, and the increasing number of prosumers reshape the electricity value chain, and distribution systems are necessarily affected. These systems were planned, developed, and operated as a passive structure for decades with low level of observability. Due to the increasing number of system states, real time operation planning and flexibility services are the key in transition to an active grid management. In this pathway, distribution system state estimation (DSSE) has a great potential, but the real demonstration of this technique is in an early stage, especially on low-voltage level. This paper focuses on the gap between theory and practice and summarizes the limits of low-voltage DSSE implementation. The literature and the main findings follow the general structure of a state estimation process (meter placement, bad data detection, observability, etc.) giving a more essential and traceable overview structure. Moreover, the paper provides a comprehensive mapping of the possible use-cases state estimation and evaluates 27 different experimental sites to conclude on the practical applicability aspects.

Preferences toward Bus Alternatives in Rural Areas of the Netherlands: A Stated Choice Experiment

Public transport in rural areas is under pressure because demand is low and dispersed. To reduce costs, flexible and on-demand services are often proposed as alternatives for conventional bus services. Conventional services are generally not suitable for rural areas, because the demand is low and dispersed. In this paper, a stated preference survey is designed to identify the preferences of rural bus users for alternative services. Other than the traditional bus, two other modes are included in this study: a demand responsive transport (DRT) service and an express bus service with bike-sharing services for last mile transport. Given the on-demand nature of these alternatives, flexibility- and reliability-related attributes are included in the stated preference survey. The results from the choice model indicate that the reliability and flexibility aspects do not have a large effect on the preference for the on-demand alternatives. Instead, cost, access and egress times, and in-vehicle time play a bigger role in individuals’ preferences toward the different alternatives. A sensitivity analysis shows that changes in the operational characteristics can make the on-demand alternatives more attractive. However, many bus users still prefer the conventional bus service over the on-demand alternatives.

Long-term price guidance mechanism of flexible energy service providers based on stochastic differential methods

With the increasing generation of renewable energy in power markets, the mismatch between power production and power demand in the power market is becoming more serious. Power prices in the short term guided the balance of load and demand. Although short-term power price guidance can provide certain benefits, short-term power price guidance disrupts the potential long-term market balance and provides sustainable development challenges. This paper adopts a stochastic differential method to guide flexible energy service providers for long-term price guidance with addressing the challenges of long-term market imbalances. After knowing power production, the stochastic differentiation method guides load power demand to follow changes in power production by a reasonable power price. The stochastic differential method utilizes energy flexibility for long-term price guidance with cost minimization in a long-term market environment. The power demand results of Zhaoqing considering the water tower model show that the long-term price guidance mechanism based on the stochastic differential method saves 12.39% of the operating cost with RMB 782.6987 million each year. The long-term price guidance based on the stochastic differential method mobilizes the energy flexibility by indirectly control the demand of flexible energy systems through reasonable price signals.

Secure Data Mobility in Cloud Computing for e-Governance Application

Due to the rapid technological advancement, traditional e-government systems are getting obsolete because of their inherent limitation of interoperability and accessibility to the highly secured and flexible e-governance services. Migration of such systems into highly secured cloud governance architecture will be a long-term viable solution. However, the adoption of distributed cloud computing has created operational and security challenges. This research work aims to bridge the gap between traditional and cloud-based e-Government systems in terms of data security based on confidentiality, interoperability, and mobility of data among distributed databases of cloud computing environments. In this work, we have created two organization databases by the use of AWS EC2 instances and classified the data based on the Risk Impact Level (RIL) of data by the use of the Metadata Attribute Value (MAV) function. To enhance further security on classified data, we take appropriate security action based on the sensitivity of the data. For the analysis purpose, we implemented different security algorithms, i.e. AES, DES, and RSA in the mobility of data between two distributed cloud databases. We measured the encryption and decryption time along with the file size of data before and after classification. AES performed better while considering the encryption time and file size, but the overall performance of RSA was better for smaller file sizes. Finally, the performance of the data mobility between two distributed clouds databases was analyzed while maintaining the sensitivity level of the data.

The vehicle routing problem with speed optimization for shared autonomous electric vehicles service

The shared autonomous electric vehicles (SAEVs), the integration of car-sharing, autonomous technology, and electrification, are anticipated to be the popular choice for future urban travel, offering more flexible demand-responsive services than traditional shared mobility. This study develops a mixed-integer linear model to optimize the SAEVs service, where the electric vehicles (EVs) are powered by the battery swapping stations (BSSs). The innovation of the model is the speed optimization on the travel arc while optimizing the service routes of the SAEVs fleet. A minimized weighted objective containing the total travel distance, the total travel time and the total energy consumption is considered. An adaptive large neighborhood search (ALNS) algorithm embedded in the speed optimization subroutine is designed to find the optimal solution. The good computational performance of ALNS is demonstrated through numerical comparisons to the solutions found by CPLEX and the best-known solutions of the similar dial-a-ride problem (DARP). The adapted set of instances was employed in the comparative experiment, from which the benefits of speed optimization for saving the objectives are obtained. Sensitivity analysis was performed on the minimum battery level, battery capacity, and the weight of energy consumption in the objective. The results provide SAEVs operators with alternative cost-saving solutions. Larger capacity batteries and lower minimum battery levels help reduce the travel distance, while smaller capacity batteries and feasible lower speeds help to save energy consumption.

Mental Health Service User and Worker Experiences of Psychosocial Support Via Telehealth Through the COVID-19 Pandemic: Qualitative Study.

BackgroundDuring the COVID-19 pandemic, we saw telehealth rapidly become the primary way to receive mental health care. International research has validated many of the benefits and challenges of telehealth known beforehand for specific population groups. However, if telehealth is to assume prominence in future mental health service delivery, greater understanding of its capacity to be used to provide psychosocial support to people with complex and enduring mental health conditions is needed.ObjectiveWe focused on an Australian community-managed provider of psychosocial intervention and support. We aimed to understand service user and worker experiences of psychosocial support via telehealth throughout the COVID-19 pandemic.MethodsThis study was jointly developed and conducted by people with lived experience of mental ill health or distress, mental health service providers, and university-based researchers. Semistructured interviews were conducted between August and November 2020 and explored participant experiences of receiving or providing psychosocial support via telehealth, including telephone, text, and videoconferencing. Qualitative data were analyzed thematically; quantitative data were collated and analyzed using descriptive statistics.ResultsService users (n=20) and workers (n=8) completed individual interviews via telephone or videoconferencing platform. Service users received psychosocial support services by telephone (12/20, 60%), by videoconferencing (6/20, 30%), and by both telephone and videoconferencing (2/20, 10%). Of note, 55% (11/20) of service user participants stated a future preference for in-person psychosocial support services, 30% (6/20) preferred to receive a mixture of in-person and telehealth, and 15% (3/20) elected telehealth only. Two meta-themes emerged as integral to worker and service user experience of telehealth during the pandemic: (1) creating safety and comfort and (2) a whole new way of working. The first meta-theme comprises subthemes relating to a sense of safety and comfort while using telehealth; including trusting in the relationship and having and exercising choice and control. The second meta-theme contains subthemes reflecting key challenges and opportunities associated with the shift from in-person psychosocial support to telehealth.ConclusionsOverall, our findings highlighted that most service users experienced telehealth positively, but this was dependent on them continuing to get the support they needed in a way that was safe and comfortable. While access difficulties of a subgroup of service users should not be ignored, most service users and workers were able to adapt to telehealth by focusing on maintaining the relationship and using choice and flexibility to maintain service delivery. Although most research participants expressed a preference for a return to in-person psychosocial support or hybrid in-person and telehealth models, there was a general recognition that intentional use of telehealth could contribute to flexible and responsive service delivery. Challenges to telehealth provision of psychosocial support identified in this study are yet to be fully understood.

Adaptation of small and medium-sized enterprises in the service sector to the conditions of Industry 4.0 and Society 4.0: evidence from the Czech Republic

Research background. The need for an in-time, efficient and effective adaptation of small and medium-sized enterprises in the service sector to the technical, economic, social and environmental conditions of Industry 4.0 and Society 4.0 is a current problem caused by the introduction of new information and communication technologies, cyber-physical systems and artificial intelligence into all economic industries and areas of human life. The advent of new technologies is changing the entire value chains of Industry 4.0 - Labor 4.0 - Education 4.0 - Thinking 4.0, creating conditions for new business models but also pressure on flexibility and personalization of services or triggering new requirements for cyber security and the interdisciplinarity of access of small and medium-sized enterprises - providers and users of services. The current global COVID-19 health pandemic (2020-2021), with its economic and social impacts on all economic industries, has accelerated the use of disruptive technologies, principles, procedures, methods and tools of Industry 4.0 and Society 4.0 and the need for the support of small and medium-sized enterprises in the service sector in adapting to changes in the business environment. Purpose of the article. The main objective of the research is to validate, evaluate and discuss the functionality and content of the created methodology of adaptation of small and medium-sized enterprises in the service sector to the implementation of the principles, procedures, methods and tools of Society 4.0 and its applicability in business practice, specifically focusing on small and medium-sized enterprises in the area of knowledge-intensive services. An integral part of the research is to assess selected negative and positive impacts of the COVID-19 pandemic on business processes in the context of issues being tackled. Methodology. The methodological procedure to validate the functionality and content of the created methodology of the adaptation of small and medium-sized enterprises in the service sector to the conditions of Society 4.0 is based on the strategy of mixed research. An explorative sequence design was used as a key design of mixed dynamically oriented research, during which quantitative data collection, analysis and evaluation formed the basis for qualitative analysis and evaluation, which resulted in the interpretation and discussion of the research results and, at the end of the paper, the formulation of the issues that will be the next area of scientific research activity of the author’s team. In the quantitative part of the research, Fisher’s exact test was used for the verification of difference statistics in data obtained from testing a sample of micro, small and medium-sized enterprises and from external project application sponsors providing services to this category of enterprises. In the qualitative part of the research, the Atlas.ti software was used for the evaluation of information obtained with respect to the methodology of adaptation of small and medium-sized enterprises for encoding, processing and interpretation of qualitative data and semantic networks were created for the individual sub-categories of evaluation. Findings &amp; Value Added. The research result proved that the adequacy of the content and functionality of the checked methodology of adaptation of SMEs strongly corresponds to the needs and requirements of micro and small enterprises in particular that conduct business in the area of knowledge-intensive services. Barriers, limits, needs and potential in individual categories of micro, small and medium-sized enterprises were identified which are related to the use of technologies and tools of Society 4.0, to new requirements for knowledge and skills qualifications of labor and to the relation of SMEs to innovations in business processes. The results of validation of the methodology of adaptation of SMEs in the knowledge-intensive service sector confirmed that the created methodology of adaptation of small and medium-sized enterprises is applicable to the implementation of the principles, procedures, methods and tools of Society 4.0. in the real conditions of business practice.

Interoperability technology of sports health monitoring equipment based on multi-sensor information fusion

Nowadays, the pace of society is accelerating, people’s living standard has been greatly improved compared with the past, and we pay more and more attention to the real-time, flexibility, and intelligence of medical services. Information fusion technology is the study of how to effectively and comprehensively utilize multi-sensor information to overcome information imperfections and uncertainties. In recent years, it has become a hotspot for research, but with the development of communication technology, electronic technology, and computers, the form of information becomes complicated and diverse in science and technology such as technology. In this case, the information obtained by a single sensor is always imperfect, so multiple sensors must be used to detect the target from multiple angles. Obtaining more dimensional information about the target and fusing it to ensure the integrity and accuracy of the information helps to accurately determine the physiological health of the human body. This paper analyzes the multi-sensor information fusion technology in detail, discusses the basic principle of multi-sensor information fusion, fusion process, fusion level, architecture and general fusion algorithm, constructs a sensor information fusion technology and its application model in biomedical detection, from the application framework, control structure of multi-sensor integrated system, information fusion, etc. The current situation of multi-sensor information fusion technology is studied in several aspects. An interoperability technology of sports health monitoring equipment based on multi-sensor information fusion is constructed. The experimental results show that the experimental efficiency is improved by 20% after using the application model in this paper, which has certain practicability.

Boat-to-Grid Electrical Energy Storage Potentials around the Largest Lake in Central Europe

With the dynamic growth in both the global hunger for electrical energy and the share of variable renewable energy sources in the energy mix, distribution networks are facing new challenges where conventional solutions may not be the best ones. The increase in load in distribution grids is routinely countered by network development and expansion, in a great part to supply for on-peak load demand, which could also be done by utilizing supplementary technologies to lessen the need for or defer such expansion. Vehicle-to-grid technology could efficiently contribute to handling this issue, as electric vehicles can potentially function as storage capacities to mitigate the fluctuations of power generation. The battery energy storage systems of hybrid or completely electric watercraft, which are becoming increasingly popular, are no exception, either. These vehicles represent a considerable potential to create more complex vehicle-to-grid solutions for countries with significant inland or seaport networks, for example, Hungary, with the largest lake of Central Europe. Since there is only deficient information on the topic, the main goal of this study was to explore the energy storage capacities of small electric boats in the context of Lake Balaton, Hungary. By this example, the paper presents the potential utilization of Europe’s significant network of sea and inland recreational ports for the purpose of energy storage. Similarly to other European countries, Hungary’s energy strategy for 2030 also includes the promotion of virtual production integration, local energy communities and micro-grid solutions. At the beginning of 2021, the small electric boats in the sailing marinas of Lake Balaton represented a cumulative energy storage capacity of 4.8 MWh, which may reach even 15.6 MWh by 2030, by the promotion of micro-grid solutions. The innovative novelty of this study is that it regards small fully electric boats not just as vehicles, but also explores their energy storage potentials. The novel goal of these examinations is to explore the volume of the energy storage potentials of the small fully electric boats around Lake Balaton, the knowledge of which may facilitate the creation of new types of flexibility services related to energy storage.

Rancangan dan Implementasi Jaringan Komputer Antar Gedung Menggunakan Koneksi Nirkabel

Wireless networks actually have a simple goal, namely providing a connection medium between users and information sources. Likewise, the purpose of creating this wireless network is internet sharing. This time, the wireless network uses an access point as a bridge, and uses a PC which will be used as a gateway router. This increases the need for a more flexible network, as well as the limited capabilities of cable networks in terms of distance. The wireless network created can overcome this problem. There are several services that this network can provide, including being able to provide flexible connection services and being able to overcome cable network problems in terms of distance. This network is able to provide connection services to clients located in the electro using a wireless network with a distance of 60 meters, and can carry out internet sharing using an access point as a bridge.

Municipal acute care teams as a flexible solution for the treatment of acutely ill patients at-home: a mixed-method study of patients’ and caregivers’ experiences with an acute care team

ObjectiveDevelopment of initiatives to reduce hospitalisations is a major focus of healthcare planning. Strengthening the community with municipal acute care teams or units is a newly implemented Danish initiative aimed...

Scalable Orchestration of Service Function Chains in NFV-Enabled Networks: A Federated Reinforcement Learning Approach

Network function virtualization (NFV) is critical to the scalability and flexibility of various network services in the form of service function chains (SFCs), which refer to a set of Virtual Network Functions (VNFs) chained in a specific order. However, the NFV performance is hard to fulfill the ever-increasing requirements of network services mainly due to the static orchestrations of SFCs. To tackle this issue, a novel Scalable SFC Orchestration (SSCO) scheme is proposed in this paper for NFV-enabled networks via federated reinforcement learning. SSCO has three remarkable characteristics distinguishing from the previous work: (1) A federated-learning-based framework is designed to train a global learning model, with time-variant local model explorations, for scalable SFC orchestration, while avoiding data sharing among stakeholders; (2) SSCO allows for parameter update among local clients and the cloud server just at the first and last epochs of each episode to ensure that distributed clients can make model optimization at a low communication cost; (3) SSCO introduces an efficient deep reinforcement learning (DRL) approach, with the local learning knowledge of available resources and instantiation cost, to map VNFs into networks flexibly. Furthermore, a loss-weight-based mechanism is proposed to generate and exploit reference samples in replay buffers for future training, avoiding the strong relevance of samples. Simulation results obtained from different working scenarios demonstrate that SSCO can significantly reduce placement errors and improve resource utilization ratio to place time-variant VNFs compared with the state-of-the-art mechanisms. Furthermore, the results show that the proposed approach can achieve desirable scalability.

The success of HIV combination prevention: The Dean Street model.

The 56 Dean Street combination prevention model, a strong engagement with the LGBTQI community and flexible services adapted to users' changing needs led to an 80% drop in HIV diagnoses in gay, bisexual and other men who have sex with men (GBMSM) from 2015 to 2017. We describe the service changes at 56 Dean Street since 2012 which resulted in an increase in the frequency of HIV testing, the introduction of pre-exposure prophylaxis, earlier HIV diagnosis and a shorter time to viral suppression in those living with HIV. This model could be adapted to deliver similar results in those settings of high HIV prevalence among GBMSM and where access to technological innovation in healthcare and engagement with the community can be achieved.