Flexible Service Research Articles

Comparative analysis of services from soft open points using cost–benefit analysis

Soft Open Points (SOPs) are power electronic-based devices which can replace Normally Open Points (NOPs) in distribution networks. They can improve network performance by enabling controllable power transfer between adjacent feeders. This flexible meshing can provide a wide range of services, including loss reduction, reduced renewables curtailment, improved reliability, reinforcement deferral, or enabling flexibility services. This paper proposes a novel framework, based on the Cost–Benefit Analysis methodology, to quantify and compare the cost-effectiveness of SOPs for providing each of these five value streams. The framework includes the development of mathematical models that encapsulate the key variables that drive competitive SOP use cases, as well as providing detailed analysis to determine quantitative estimates for each of the parameters. Results suggest that, whilst all services could be cost-effective, that reinforcement deferral and reduced DG curtailment are most likely to find wide usage. It is also suggested that the fast response time of SOPs as compared to conventional NOPs is unlikely to be a viable value proposition for improving reliability via conventional loss of load metrics such as energy not supplied. A detailed case study demonstrates that in marginal cases, where a SOP has a similar system net benefit compared to Business-as-Usual, that all services need to be considered rather than just single value streams in isolation. It is concluded from the research that there are multiple potential competitive applications for SOPs in future distribution networks.

Blockchain and Machine Learning for Future Smart Grids: A Review

Developments such as the increasing electrical energy demand, growth of renewable energy sources, cyber–physical security threats, increased penetration of electric vehicles (EVs), and unpredictable behavior of prosumers and EV users pose a range of challenges to the electric power system. To address these challenges, a decentralized system using blockchain technology and machine learning techniques for secure communication, distributed energy management and decentralized energy trading between prosumers is required. Blockchain enables secure distributed trust platforms, addresses optimization and reliability challenges, and allows P2P distributed energy exchange as well as flexibility services between customers. On the other hand, machine learning techniques enable intelligent smart grid operations by using prediction models and big data analysis. Motivated from these facts, in this review, we examine the potential of combining blockchain technology and machine learning techniques in the development of smart grid and investigate the benefits achieved by using both techniques for the future smart grid scenario. Further, we discuss research challenges and future research directions of applying blockchain and machine learning techniques for smart grids both individually as well as combining them together. The identified areas that require significant research are demand management in power grids, improving the security of grids with better consensus mechanisms, electric vehicle charging systems, scheduling of the entire grid system, designing secure microgrids, and the interconnection of different blockchain networks.

Fast Resource Allocation for Resilient Service Coordination in an NFV-Enabled Internet-of-Things System

Network Functions Virtualization (NFV) is a new way of leveraging an Internet-of-Things (IoT) system to provide real-time and highly flexible service creation. In an NFV-enabled Internet-of-Things (NIoT) system, several IoT functions implemented as Virtual Network Functions can be linked as a service function chain to build a customized IoT service quickly. It is important for an IoT service to be able to recover from a failure. However, the supply of a resilient IoT service in an NIoT system is challenging due to the coordination of distributed VNF instances. In this paper, we formulate the problem of resilient service coordination in an NIoT system as a mixed-integer linear programming model, namely RSOd. The model offers the optimal resource allocation for minimizing service disruption when a failure happens at a node of an NIoT system. We also develop two modified versions of RSOd for different use cases required by an IoT provider. Further, two approximation algorithms are proposed to provide a resilient service for a large-scale NIoT system. The evaluation results show that RSOd and its modified versions produce the optimal resource allocation in significantly reduced time compared to previous work. The results suggest that an IoT provider should carefully select an appropriate resource allocation strategy as it has to pay a resource cost to minimize the service disruption. The results also show that our proposed priority-based heuristic algorithm outperforms an approximation algorithm based on Simulated Annealingin terms of the service disruption and computation time.

Designing demand responsive transport services in small-sized cities using an agent-based model

This paper presents an agent-based model (ABM) to simulate and compare two different operation strategies of a public transport service in small-sized cities, namely a fixed-route transit (FRT) and a demand-responsive transport (DRT) service, under varying demand rates and supply configurations. The ABM builds upon a previous work by the Authors, where flexible and feeder services of a Mass Rapid Transit system were simulated. In this paper, instead of a many-to-one pattern typical of a feeder service, we considered a many-to-many one. The objective is to investigate the conditions that make a DRT more attractive than a FRT in small-sized cities and guide its design considering the demand fluctuation, land-use pattern, service constraints and passenger preferences. A dispatching algorithm for the DRT allows to assign each new trip request to a vehicle, and a couple of origin and destination stops, updating the vehicle schedule in real time. The service includes fixed and virtual stops, allowing request consolidation and balancing operator-related (cost of the service) and user-related (quality of service) needs. The model is applied to Vittoria (Italy), a small city with 60,000 residents in Southern Italy where most trips are made by car, also due to the absence of an urban public transport service. First results highlight the benefits of providing a flexible service compared to a fixed one to minimize detours, waiting times and walking distances experienced by passengers while allowing for a higher shareability and efficiency of the service.

Battery energy storage systems for ancillary services in Renewable energy communities

This document presents a study on the use of battery energy storage systems in a proposed renewable energy community in Savona, UNIGE Campus. The study compares the outcomes of cooperative approaches with and without flexibility services to a scenario where users do not cooperate. The study concludes that storage systems of relevant size can create new flexible instruments for the power grid and a powerful tool for citizens, which could make the investment into BESS financially viable. The study also suggests that greater transparency and information on hourly energy sales and purchase prices would facilitate awareness among community members and at the same time stimulate discussion of alternative technologies such as batteries to cover the most expensive nighttime periods for users. Overall, the study highlights the potential of battery systems in renewable energy communities in Italy and provides insights into the importance of coupling flexible services with capacity-building activities and awareness campaigns to promote demand-side-response activities and storage technologies.

Multi-Period Hybrid AC/DC-OPF Model for Flexibility Market Clearing With Seamless TSO-DSO Coordination

Unlocking flexibility assets from the consumer side and developing a well-functioning flexibility market (FM) is crucial to address the oncoming challenges in the EU power grid. In these conditions, the coordination between distribution system operators (DSOs) and transmission system operators (TSOs) remains a critical task for optimally harvesting flexibility services. For that reason, this paper focuses on investigating the instruments for FM market design and modelling, which ensures proper TSO-DSO coordination. First, the paper introduces the scheme and strategy for FM operation and coordination in Norwegian conditions. After, the multi-period hybrid AC/DC-OPF model for FM modelling is presented. This model combines optimal power flow (OPF) methods consisting of a second-order cone (SOC)-ACOPF, which is applied for the distribution grid and DC-OPF for the transmission grid. The model was validated and tested based on grid configurations from existing projects, where it showed high accuracy and applicability. Finally, an example of solving grid challenges in a distribution grid with the help of flexibility services was presented. The outcomes of this paper propose valuable tools for establishing an effective flexibility exchange mechanism between TSO and DSO.

The Impact of Organizational Culture in the Public Management Leadership Style

The business environment has been developing a growing interest in the concept of organizational culture, as it plays an important role in the functioning and results of organizations, but the public sector, although it has reformed over the years, these same reforms have not contributed to a competitive sector in the area of Human Resources and innovation. It continues to be a cumbersome and bureaucratic Administration, leaving little room to create commitment and sustainable cohesion with regard to human relations, procrastinating change and adapting to creativity and innovation. It is in these points that the weaknesses of the public service are found, with a strong conservative culture of legal imposition, although some authors defend that the restructuring has allowed some flexibility in the approximation to the principles of private management. Thus, the objective of this investigation is to contribute to a better understanding of the cultural characteristics of public organizations and their relationship with leadership. This study uses the exploratory sequential mixed method, starting with a qualitative data analysis, based on an exploratory interview with a general and open question, which gave rise to the problem and defined the literature to be applied. In the empirical analysis, real quantitative data were applied, acquired through a questionnaire survey, with 100 questions indexed to the four quadrants of Robert Quinn’s model of contrasting values. In short, the main conclusions obtained show a bureaucratic Public Administration, with a need for change and innovation, to respond to current demands, in order to provide quality and flexible services. The results demonstrate an organizational culture of rules, hierarchical, internally focused and with the structure in control. Managers corroborate the results of this culture, focused on internal processes and centred on the normal state of leadership.

Parents' experiences of paediatric palliative care in the community healthcare system: a qualitative study.

Having a child with a life-limiting illness is a situation that is relatively rare and represents a multidimensional burden on the family. Paediatric palliative care (PPC) aims to maintain the quality of life for the ill child and the family. Traditionally, most PPC has been provided at a specialist healthcare level, but research indicates that most families wish to spend as much time at home as possible. However, we have limited knowledge of PPC in community healthcare, especially from the parent's perspective. This knowledge is important to provide optimal home-based PPC. To explore parents' experiences of PPC within the community healthcare system. Qualitative study with an interpretive descriptive design. In all, 11 parents of children with different life-limiting illnesses were interviewed after the child's death using a semi-structured interview guideline. Data were analysed using systematic text condensation. Consolidated criteria for reporting qualitative research (QOREQ) was followed. The parents' experiences were captured in five main themes: (i) 'Interaction with hospital and community services', (ii) 'Parents did not always get the help they needed', (iii) 'The child's needs became increasingly complex', (iv) 'When the end came' and (v) 'The parents asked for an ordinary life in an unordinary situation'. Each main theme was further elaborated by two subthemes. Overall, the parents experienced PPC in the community as limited and fragile, and as lacking flexibility, coordination and professional competence related to the children's complex needs. There appears to be potential for improvement in PPC through improved care coordination between the hospital and the community healthcare services, involving the community healthcare system at an early timepoint in the illness trajectory, including a family focus, and providing accessibility, flexibility and care coordination of community services. The study is registered in the institutional system for research project (RETTE; ID number F2082).

Blockchain-Based Network Slice Broker to Facilitate Factory-As-a-Service

The novel concept of factory-as-a-service (FaaS) allows the agility of adapting the manufacturing process by identifying the industry’s supply chain and user requirements. To cater to FaaS, flexibility in networking and cloud services is a must. 5G network slice broker (NSB) is a third-party mediator that caters to networking resource demand from clients to the service providers. Thus, this article introduces a secure blockchain-based NSB to facilitate FaaS. The proposed secure NSB (SNSB) provides secure, cognitive, and distributed network services for resource allocation and security service level agreement (SSLA) formation with coordination of slice managers and SSLA managers. In SNSB, we introduce a federated slice selection algorithm with Stackelberg game model and reinforcement learning algorithm to compute the real time and the optimal unit price and demand level. We provide an extensive implementation and performance evaluation of SNSB using the slice manager and a custom SSLA manager.

ТУРОПЕРЕЙТИНГОВІ ІННОВАЦІЇ У СФЕРІ ЕКСКУРСІЙНОГО ТУРИЗМУ: ВИКЛИКИ ТА ПЕРСПЕКТИВИ

The article delves into the exploration of tour operating innovations utilized to foster the growth of excursion tourism. In this comprehensive analysis, various aspects are examined, encompassing cutting-edge technologies, novel marketing and promotional strategies, the utilization of online platforms, and digital solutions. These innovations are aimed at augmenting the quality and accessibility of excursion services. The article places significant emphasis on distinguishing traditional tour operating services from the avant-garde approaches within the realm of excursion tourism, particularly within the context of contemporary tourism development. One of the key findings of the study is that innovative approaches in excursion tourism exhibit distinct characteristics, such as heightened flexibility, individualization, and personalization of services. Additionally, interactive technologies play a pivotal role, facilitating immersive experiences for tourists. Another noteworthy aspect is the involvement of the local community in the organization and execution of excursions, fostering a deeper connection between tourists and the destination. Furthermore, the article sheds light on the imminent challenges that tour operators may face when implementing these innovations within the excursion tourism sector. Potential hurdles include technological and organizational barriers that may arise during the transition to new business models and the adoption of new information systems. Despite these challenges, the article emphasizes the prospects and opportunities that arise for tour operators through the integration of innovation in the development of excursion tourism. These prospects encompass an array of advantages, such as the ability to enhance service quality, attract new target tourist groups, and leverage data analytics to optimize marketing efficiency and route planning. In conclusion, the article affirms that the implementation of innovative solutions in the operations of tour operator companies in Ukraine yields several potential advantages. These encompass heightened competitiveness, increased profitability for tour operators, and an overall improvement in tourist satisfaction, ultimately enhancing their travel experiences.

<i>c</i>-SNE: Deep Cross-modal Retrieval based on Subjective Information using Stochastic Neighbor Embedding

Cross-modal information retrieval based on subjective information aims to enable flexible media retrieval services, such as allowing users to specify, for example, an image to search audio clips. The resulting audio clips should have an impression similar to the specified image. Existing methods focus on building cross-media cross-modal relationships using objective information (such as the standard caption). However, such a relation can be built only between the pieces of media that are originally related, which limits the flexibility of cross-modal media retrieval. This research leverages subjective information in the media clips for similarity calculation to achieve more flexibility. We propose a novel cross-modal stochastic neighbor embedding technique called c-SNE. c-SNE can extract features of subjective information from pieces of media and map them in the common embedding space. It is a learning technique to bridge the heterogeneous gap between the modal distributions using label-weighted SNE. It allows users to find the media that share the same subjective information with a query medium. Our experimental results on the benchmark datasets demonstrate that the proposed method effectively performs in cross-modal distribution alignment and retrieval. Furthermore, our user study with ten users with 600 data points confirmed that c-SNE outperforms three related methods in the actual usage situation from the users' perspective.

Multi-Agent Deep Reinforcement Learning Based Transmission Latency Minimization for Delay-Sensitive Cognitive Satellite-UAV Networks

With the ubiquitous deployment of a massive number of Internet-of-Things (IoT) devices, the satellite-aerial networks are becoming a promising candidate to provide flexible and seamless service for IoT applications. Concerning about the spectrum scarcity issue, we present a cognitive satellite-aerial network where the multiple unmanned aerial vehicles (UAVs) can share spectrum with the satellite without interfering with satellite communications. To further improve spectral efficiency, non-orthogonal multiple access (NOMA) technique is adopted in this network. Considering the delay-sensitive quality-of-service (QoS) requirement, a joint trajectory and power optimization problem is formulated to minimize the total transmission latency over a long-term task period. In order to reduce the computational complexity and ease the burden of information exchange by using the centralized DRL methods, we propose a multi-agent deep deterministic policy gradient (MADDPG) based algorithm which adopts the framework of centralized training with decentralized execution to solve the sophisticated problem. The simulation results show the proposed algorithm can achieve satisfactory performance through joint trajectory control and power allocation for the UAVs compared with other methods.

Networking Controller Based Real Time Traffic Prediction in Clustered Vehicular Adhoc Networks

The vehicular ad hoc network (VANET) is an emerging network technology that has gained popularity because to its low cost, flexibility, and seamless services. Software defined networking (SDN) technology plays a critical role in network administration in the future generation of VANET with fifth generation (5G) networks. Regardless of the benefits of VANET, energy economy and traffic control are significant architectural challenges. Accurate and real-time traffic flow prediction (TFP) becomes critical for managing traffic effectively in the VANET. SDN controllers are a critical issue in VANET, which has garnered much interest in recent years. With this objective, this study develops the SDNTFP-C technique, a revolutionary SDN controller-based real-time traffic flow forecasting technique for clustered VANETs. The proposed SDNTFP-C technique combines the SDN controller’s scalability, flexibility, and adaptability with deep learning (DL) models. Additionally, a novel arithmetic optimization-based clustering technique (AOCA) is developed to cluster automobiles in a VANET. The TFP procedure is then performed using a hybrid convolutional neural network model with attention-based bidirectional long short-term memory (HCNN-ABLSTM). To optimise the performance of the HCNN-ABLSTM model, the dingo optimization technique was used to tune the hyperparameters (DOA). The experimental results analysis reveals that the suggested method outperforms other current techniques on a variety of evaluation metrics.

Deep Cross-Modal Hashing Based on Semantic Consistent Ranking

The amount of multi-modal data available on the Internet is enormous. Cross-modal hash retrieval maps heterogeneous cross-modal data into a single Hamming space to offer fast and flexible retrieval services. However, existing cross-modal methods mainly rely on the feature-level similarity between multi-modal data and ignore the relationship between relative rankings and label-level fine-grained similarity of neighboring instances. To overcome these issues, we propose a novel <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</u> eep <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u> ross-modal <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</u> ashing based on <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> emantic <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u> onsistent <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</u> anking (DCH-SCR) that comprehensively investigates the intra-modal semantic similarity relationship. Firstly, to the best of our knowledge, it is an early attempt to preserve semantic similarity for cross-modal hashing retrieval by combining label-level and feature-level information. Secondly, the inherent gap between modalities is narrowed by developing a ranking alignment loss function. Thirdly, the compact and efficient hash codes are optimized based on the common semantic space. Finally, we use the gradient to specify the optimization direction and introduce the Normalized Discounted Cumulative Gain (NDCG) to achieve varying optimization strengths for data pairs with different similarities. Extensive experiments on three real-world image-text retrieval datasets demonstrate the superiority of DCH-SCR over several state-of-the-art cross-modal retrieval methods.

Ligurian Internal Areas and Demand Responsive Transport: an innovative approach to tackle social exclusion and to re-design sustainable accessibility

Making public transport more flexible, sustainable and efficient has been one the main goals of the last decades for Public Administrations in order to improve urban performances in mobility sector, that is responsible for a big share of pollutant emissions and deeply affects quality life level. Anyway, recently, attention grew on the fact that this topic does not belong to urban areas only and needs tailored measures to be effective: accessibility for peripherical and scarcely populated areas represents the pre-requisite of citizenship rights itself. Basic services (health, education above all) are getting more and more concentrated in order to reduce costs and this means that non-urban residents have to make long trips to reach them. Public Transport itself has become no longer sustainable in these territories because of the small population and low-density levels of transport demand (the so-called “low-demand areas”). That is the reason why flexible and customizable solutions (Demand Responsive Transport-DRT, for instance) could meet the needs of these remote inhabitants, thus improving social inclusion and being the first step to more complex regeneration processes. Italian National Internal Areas Strategy (SNAI) focuses on mobility actions, too. Many flexible transport services have been proposed in order to balance accessibility rights of local residents and economical and environmental sustainability of the service itself. This paper presents the Ligurian Internal Areas experiences (and specifically Antola-Tigullio strategy), where DRT services implementation is currently taking place to tackle marginalization process: referring to these areas non-traditional transport services allow to cover larger areas and longer parts of the day, without adding new costs (service is active only when users need it), thus developing a new mobility scheme.

Cost-Efficient Virtual Network Function Placement in an Industrial Edge System: A Proposed Method

Edge computing has been a promising framework to provide effective computing services for edge users in most industrial environments. Due to the limited resources at the edge, it is not suitable for massive requirements such as the virtual reality process, communication, and so on. Cloud computing can provide sufficient resources for those requirements, but it has some latency lags. Industrial edge clouds enable flexible service providers for industrial applications by merging edge and cloud computing. The virtual network function (VNF) is one approach, while placement is a dominant solution. However, this solution may increase the cost of communication as the nodes are more distant. In this article, we investigate the resource allocation problem for VNF placement in industrial edge systems. The aim is to minimize the overall cost of the whole placement procedure, including operating cost, communication cost, and placement cost. We formulate this VNF placement problem as an integer programming problem, and due to the complexity of the problem, we propose a heuristic method to solve it. Numerical simulation shows that our proposed method can significantly cut down the cost while meeting the multiple resource constraints.

Flexibility Aggregation Perimeter for Ancillary Services in Radial Distribution Systems

The flexibility provided by prosumers is recognized as a key element for the management of the electrical grids, in particular distribution grids, in the changing scenario of electricity generation and consumption. The flexibility useful to Distribution System Operators (DSOs) is strongly local, and it is necessary to identify the busses of the grid where the flexibility can be usefully provided for a service requested by the DSO. For this purpose, the concept of flexibility perimeter is adopted; it is defined as a set of busses such that flexibility can be provided by any set of prosumers within it while the grid operation remains secure. The flexibility perimeter is a relevant concept for flexibility markets; in particular, for aggregators, since the flexibility provided by the prosumers within the perimeter can be offered as a single product to participants to electricity markets requiring grid services. The identification of flexibility aggregation perimeters for congestion-relief services in a radial grid is straightforward; for flexibility services intended to solve voltage profile problems, the identification of perimeters is not easy, even in radial grids. The paper proposes a method for the identification and evaluation of flexibility perimeters for grid services in radial distribution grids, based on the concept of Load Area; it can help decision-makers choose a compromise between technical and market efficiency of the aggregation perimeter dimension. For voltage support services, the dimension of the perimeter oppositely influences the technical soundness and the market efficiency of the aggregation; the influence is evaluated quantitatively. The results of two case studies show the features of the method.

Enhanced Coyote Optimization with Deep Learning Based Cloud-Intrusion Detection System

Cloud Computing (CC) is the preference of all information technology (IT) organizations as it offers pay-per-use based and flexible services to its users. But the privacy and security become the main hindrances in its achievement due to distributed and open architecture that is prone to intruders. Intrusion Detection System (IDS) refers to one of the commonly utilized system for detecting attacks on cloud. IDS proves to be an effective and promising technique, that identifies malicious activities and known threats by observing traffic data in computers, and warnings are given when such threats were identified. The current mainstream IDS are assisted with machine learning (ML) but have issues of low detection rates and demanded wide feature engineering. This article devises an Enhanced Coyote Optimization with Deep Learning based Intrusion Detection System for Cloud Security (ECODL-IDSCS) model. The ECODL-IDSCS model initially addresses the class imbalance data problem by the use of Adaptive Synthetic (ADASYN) technique. For detecting and classification of intrusions, long short term memory (LSTM) model is exploited. In addition, ECO algorithm is derived to optimally fine tune the hyperparameters related to the LSTM model to enhance its detection efficiency in the cloud environment. Once the presented ECODL-IDSCS model is tested on benchmark dataset, the experimental results show the promising performance of the ECODL-IDSCS model over the existing IDS models.

An Effective Method to Estimate the Aggregated Flexibility at Distribution Level

The exploitation of flexibility services provided by distributed energy resources (DERs) located in the distribution grid becomes increasingly important for the secure and reliable operation of power systems. These resources are typically small-scale and located close to end-users, however their aggregated flexibility can offset the imbalance caused by the stochastic variations of the renewable power sources. This paper proposes a methodology to estimate with increased accuracy the available flexibility of DERs in an Active Distribution Grid (ADG) at the points of interconnection with the transmission system. An optimization-based approach is used to estimate the available maximum active and reactive power flexibility at each TSO-DSO substation for specific directions formed by constant active to reactive power ratios. In order to keep calculations manageable and calculate the available flexibility with high accuracy, the search directions are sampled from a distribution that is iteratively updated at each step. Three variations are proposed for the angle sampling, both in the angle domain and in the p-q domain. Each variation is tested on a modified 18-bus radial distribution system and its effectiveness and convergence is compared.

Estimating Risk-aware Flexibility Areas for Electric Vehicle Charging Pools via AC Stochastic Optimal Power Flow

This paper introduces a stochastic AC-OPF (SOPF) for the flexibility management of electric vehicle (EV) charging pools in distribution networks under uncertainty. The SOPF considers discrete utility functions from charging pools as a compensation mechanism for eventual energy not served to their charging tasks. An application of the proposed SOPF is described where a distribution system operator (DSO) requires flexibility to each charging pool in a day-ahead time frame, minimizing the cost for flexibility while guaranteeing technical limits. Flexibility areas are defined for each charging pool and calculated as a function of a risk parameter involving the solution's uncertainty. Results show that all players can benefit from this approach, i.e., the DSO obtains a risk-aware solution, while charging pools/tasks perceive a reduction in the total energy payment due to flexibility services.