Jolanta Latosińska, Michał Kopacz, Piotr Olczak
et al.
Human activity impacts the natural environment. One example of such an impact is energy production, including energy from renewable sources. The aim of this study was to analyse and assess changes in the state of the environment in 2008, 2015 and 2023, resulting from the development and structure of renewable energy sources in EU countries. Three research questions were formulated: Question 1 (Q1). Is the state of the environment in most EU countries characterised by variability in terms of the level of renewable energy development? Question 2 (Q2). Has the composition of the group of EU countries with the highest environmental status changed? Question 3 (Q3). Is the group of EU countries with the highest environmental status characterised by a diverse structure of renewable energy sources used? The study covers three key periods: 2008, 2015 and 2023. This approach allows for the identification of the impact of crisis factors on the relationship between the energy transition and environmental status. The evaluation applied the TOPSIS, EDAS and Ward’s methods. Based on a substantive and formal analysis, diagnostic variables were selected: 18 describing the structure and level of RES development, 7 economic indicators and 11 reflecting the environmental status of EU countries. The selection criterion was data availability, with sources drawn from the EUROSTAT, IRENA and World Bank Group databases. The results show that the main leaders were Italy, Sweden, France and Germany, with Austria and Denmark maintaining high positions only in 2008. Italy took the lead in 2015 and retained it in 2023 thanks to extensive emission reductions, while Finland joined the top group. Poland and Lithuania ranked last in 2015 and 2023. A growing gap was also observed between the leaders and the lowest-performing countries. Among the highest-ranked countries, hydropower was the dominant RES, while in Germany and Denmark, wind energy and biofuels also played a key role. Cluster analysis using Ward’s method confirmed the diversity of environmental and energy profiles, as well as Belgium’s distinct position. The study confirms the instability of most EU countries’ positions, the persistence of a small group of leaders and widening disparities in sustainable environmental development within the EU.
Diego Firmenich, Leandro Antonelli, Bruno Pazos
et al.
User stories are one of the most widely used artifacts in the software industry to define functional requirements. In parallel, the use of high-fidelity mockups facilitates end-user participation in defining their needs. In this work, we explore how combining these techniques with large language models (LLMs) enables agile and automated generation of user stories from mockups. To this end, we present a case study that analyzes the ability of LLMs to extract user stories from high-fidelity mockups, both with and without the inclusion of a glossary of the Language Extended Lexicon (LEL) in the prompts. Our results demonstrate that incorporating the LEL significantly enhances the accuracy and suitability of the generated user stories. This approach represents a step forward in the integration of AI into requirements engineering, with the potential to improve communication between users and developers.
To address energy management challenges for intelligent connected automated range-extended electric vehicles under vehicle-road cooperative environments, a hierarchical energy management strategy (EMS) based on speed prediction is proposed from the perspective of multi-objective optimization (MOO), with comprehensive system performance being significantly enhanced. Focusing on connected car-following scenarios, acceleration sequence prediction is performed based on Kalman filtering and preceding vehicle acceleration. A dual-layer optimization strategy is subsequently developed: in the upper layer, optimal speed curves are planned based on road network topology and preceding vehicle trajectories, while in the lower layer, coordinated multi-power source allocation is achieved through <i>EMS<sub>MPC-P</sub></i>, a Bayesian-optimized model predictive EMS based on Pontryagin’ s minimum principle (PMP). A MOO model is ultimately formulated to enhance comprehensive system performance. Simulation and bench test results demonstrate that with <i>SoC</i><sub>0</sub> = 0.4, 7.69% and 5.13% improvement in fuel economy is achieved by <i>EMS<sub>MPC-P</sub></i> compared to the charge depleting-charge sustaining (CD-CS) method and the charge depleting-blend (CD-Blend) method. Travel time reductions of 62.2% and 58.7% are observed versus CD-CS and CD-Blend. Battery lifespan degradation is mitigated by 16.18% and 5.89% relative to CD-CS and CD-Blend, demonstrating the method’s marked advantages in improving traffic efficiency, safety, battery life maintenance, and fuel economy. This study not only establishes a technical paradigm with theoretical depth and engineering applicability for EMS, but also quantitatively reveals intrinsic mechanisms underlying long-term prediction accuracy enhancement through data analysis, providing critical guidance for future vehicle–road–cloud collaborative system development.
The circular economy implementation in the built environment is hindered by the complexity of CE strategies and unique nature of the construction industry. Digital technologies have been explored as promising solutions to aid decision making and enable circular solutions in the architecture, engineering, and construction sector. The literature on both circular economy and digital technology fields has grown exponentially in the past few years, and there is a need for a comprehensive review of the state-of-the-art applications, integrations, potential, and limitations of digital technologies in the circular economy context. Through a systematic literature review, this study identified ten key digital technologies to enable circularity in the building sector: building information modeling, spatial data acquisition, artificial intelligence and machine learning, Internet of Things, blockchain, digital twin, augmented and virtual realities, digital platform/marketplace, material passports, and additive manufacturing and digital fabrication. In this study, we review current applications, discuss their integrations, match digital technology opportunities with circular economy barriers, and map the digital technologies applications along a building’s lifecycle. Blockchain and material passport technologies demonstrated potential to enable circular economy strategies throughout the whole building’s lifecycle, but their application remains limited in the construction industry. Building information modeling was found to be at the core of most technological integrations, but more research is needed to understand the impact of such integrations in supporting circular economy policies, standards, and assessment methods. Finally, collaborative research efforts are needed to unveil the risks of digitalization in the built environment, including risks concerning privacy and cybersecurity.
Praveen Nagarajan Durai, K. Arunprasath, Divya Divakaran
et al.
Composite materials have become indispensable across a wide array of sectors, ranging from aerospace and automotive to energy, marine engineering, infrastructure, and architecture, thanks to their exceptional strength-to-weight ratios. In the automotive and marine industry, high-performance composites are replacing conventional materials with enhanced durability and corrosion resistance. As demand for lighter, stronger, and more durable materials grows, composites continue to outpace traditional metals and ceramics. Despite this rapid expansion, the global supply of natural fibers cannot keep pace with burgeoning demand, which is increasing at an estimated rate of 60% per year. To harness their full potential, fibers must undergo a comprehensive process. Physico-chemical, thermal, mechanical, and morphological characterization; surface treatments may be necessary to remove impurities or enhance interfacial adhesion when fibers exhibit insufficient roughness. To address both material scarcity and environmental concerns, this study identifies and characterizes a novel lignocellulosic fiber source derived from agricultural waste. By transforming residual biomass into high-value reinforcement materials, we not only expand the palette of natural fibers available for composite manufacturing but also contribute to waste reduction and promote a circular ‘waste-to-materials’ economy. This approach promises significant environmental benefits, paving the way for greener using an agricultural waste for sustainable applications.
Embankment structures in civil engineering, such as earth dams and fluvial dikes, have a crucial role in society. These structures, often used for water storage and mining tailing containment, are cost-effective due to their reliance on locally sourced materials. While the failure of concrete structures is not so frequent but often lead to severe consequences, embankment structures, particularly fluvial dikes, are more prone to breach and the consequences vary from mild to catastrophic, depending on the proximity to human populations. Worldwide, some fluvial dike failures have resulted in catastrophic outcomes for human lives, the local economy and the environment. This paper aims to develop a methodology to calculate in situ breach outflow hydrographs, resorting to real-time, non-intrusive and friendly access technology. The goal is to provide a practical platform for developing and testing integrated systems applicable to prototype failure cases. An accurate, real-time hydrograph estimation capacity improves risk assessment. The proposed methodology deploys, in a medium-scale experimental facility, common technology and data processing techniques to characterize the evolution of a fluvial dike failure. The morphodynamic and hydrodynamic components influencing the in situ breach outflow hydrograph are assessed by characterizing, in real-time, the breach morphology at the surface and underwater, the surface velocity maps and the corresponding cartesian coordinates.
In this short paper, we define the investment ability of data investors in the data economy and its heterogeneity. We further construct an analytical heterogeneous agent model to demonstrate that differences in data investment ability lead to divergent economic results for data investors. The analytical results prove that: Investors with higher data investment ability can obtain greater utility through data investment, and thus have stronger incentives to invest in a larger scale of data to achieve higher productivity, technological progress, and experience lower financial frictions. We aim to propose a prerequisite theory that extends the analytical framework of the data economy from the currently prevalent representative agent model to a heterogeneous agent model.
Classification is a crucial task for reintroducing end-of-life fabrics as raw materials in a circular process, thus reducing reliance on dyeing processes. In this context, this review explores the evolution of automated and semi-automated colour classification methods, emphasizing the transition from deterministic techniques to advanced methods, with a focus on machine learning, deep learning, and particularly Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). These technologies show potential for improving accuracy and efficiency. The results highlight the need for enriched datasets, deeper AI integration into industrial processes, and alignment with circular economy objectives to enhance sustainability without compromising industrial performance. Tested against a case study, the different architectures proved to be effective in classification with better performance reached by CNN-based methods, which consistently outperforms other methods in most colour families, with an average accuracy of 86.1%, indicating its robustness and adaptability for this task.
A novel surface plasmon resonance (SPR) refractive index (RI) sensor based on the D-type dual-mode photonic crystal fiber (PCF) is proposed. The sensor employs a side-polished few-mode PCF that facilitates the transmission of the fundamental and second-order modes, with an integrated microfluidic channel positioned directly above the fiber core. This design minimizes the distance to the analyte and maximizes the interaction between the optical field and the analyte, thereby enhancing the SPR effect and resonance loss for improved sensing performance. Au-TiO<sub>2</sub> dual-layer material was coated on the surface of a microfluidic channel to enhance the penetration depth of the core evanescent field and tune the resonance wavelength to the near-infrared band, meeting the special needs of chemical and biomedical detection fields. The finite element method was utilized to systematically investigate the coupling characteristics between various modes and surface plasmon polariton (SPP) modes, as well as the impact of structural parameters on the sensor performance. The results indicate that the LP<sub>11b_y</sub> mode exhibits greater wavelength sensitivity than the HE<sub>11_y</sub> mode, with a maximum sensitivity of 33,000 nm/RIU and an average sensitivity of 8272.7 nm/RIU in the RI sensing range of 1.25–1.36, which is higher than the maximum sensitivity of 16,000 nm/RIU and average sensitivity of 5666.7 nm/RIU for the HE<sub>11b_y</sub> mode. It is believed that the proposed PCF-SPR sensor features both high sensitivity and high resolution, which will become a critical device for wide RI detection in mid-infrared fields.
Предметом дослідження в статті є ефективність адаптивних методів попереднього оброблення медичних зображень, зокрема білатерального фільтра та модифікованого CLAHE, у задачах сегментації стоматологічних рентгенівських знімків. Ці методи дають змогу зберігати важливі деталі зображення та ефективно зменшувати шум, навіть у разі високої варіативності зображень, що надходять із різних джерел. Мета роботи – розроблення покращень методів попереднього оброблення медичних зображень, а саме білатерального фільтра та CLAHE, з огляду на контекст зображення. Дослідження спрямоване на підвищення ефективності сегментації медичних знімків за допомогою збереження важливих деталей і зменшення впливу шуму й артефактів у зображеннях із різних джерел. У статті розв’язуються завдання: експериментальне порівняння розроблених адаптивних методів попереднього оброблення з традиційними підходами; оцінювання ефективності сегментації за допомогою метрик, таких як коефіцієнт Дайса (Dice Score), коефіцієнт Жаккара (IoU Score), влучність (Precision) та чутливість / повнота (Recall); аналіз впливу попереднього оброблення на якість сегментації. Впроваджено такі методи: математичне моделювання, тренування нейронної мережі на основі моделі U-Net із попередньо натренованим енкодером timm-resnest101e, масштабування зображень до 512×512 пікселів, навчання з фіксованим learning rate 0.001. Досягнуті результати. Під час експериментального порівняння розроблених адаптивних методів попереднього оброблення з традиційними підходами встановлено, що комбіноване використання адаптивного білатерального фільтра та модифікованого CLAHE забезпечило найвищі показники якості сегментації. Зокрема, за метриками коефіцієнт Дайса (Dice Score) 0.9603 та коефіцієнт Жаккара (IoU Score) 0.94501 ці методи перевершили традиційні, що свідчить про їх ефективність у збереженні контурів об’єктів та зниженні шуму. Висновки. Застосування розроблених адаптивних методів попереднього оброблення суттєво покращує точність сегментації медичних зображень. Комбінований підхід, що передбачає адаптивний білатеральний фільтр і модифікований CLAHE, є найбільш ефективним для завдань медичної візуалізації, що підвищує точність діагностики та надійність автоматизованих систем підтримки прийняття рішень.
The oil and gas industry has long dominated Nigeria’s blue economic growth. However, a new frontier that can play sustainable economic growth in the maritime industry and therefore national economic leverage is offshore aquaculture. Hence, this paper aims to provide relevant information on the economic analysis of the potential offshore aquaculture practice to enhance the diversification of the blue economy in Nigeria using secondary data. Sensitivity analysis considering input variations of up to 45% is also performed to take care of the unforeseen. Offshore aquaculture in this paper refers to fish production in the Open Ocean using large cages/nets. Ten (10) fish cages of 37500-fish capacity per cage were hypothetically designed with fiberglass materials and installed in Escravos offshore. A mortality of 30% was used with the current prices of other required investments. The analysis recorded a breakeven period of 2 years and NPV value of over one trillion Naira in 9 years indicating massive profitability comparable to the oil and gas industry! Sensitivity analysis identified mortality/loss of fish and falling prices of fish as events that could adversely affect the investment. It is suggested that the investment should be done with experienced professionals in fishery, offshore engineering, and cost control.
Harjeet Singh, Komal Janiyani, Ajit Gangawane
et al.
Abstract The increasing global energy demand and environmental concerns have highlighted the need for sustainable and renewable energy sources. Lignocellulosic biomass (LCB), rich in cellulose, hemicellulose, and lignin, is a promising resource for biofuel production. However, the recalcitrant nature of lignin poses a significant challenge by obstructing effective LCB decomposition. This review provides a comprehensive analysis of recent advancements in genetic and metabolic engineering techniques, focusing on directed and random mutagenesis to enhance cellulase production in fungi. It explores how these techniques can overcome challenges in lignin degradation and improve LCB conversion efficiency. Lignin's high resistance to degradation, due to its complex association with cellulose and hemicelluloses, necessitates the development of advanced fungal strains through mutagenesis. Fungi, which are efficient lignin degraders, benefit from these practices to enhance enzyme production and address environmental pollution from burning LCB. The review emphasizes engineering cellulolytic fungi through mutagenesis, gene-editing, and synthetic biology, highlighting CRISPR-Cas innovations and nanoparticle-based delivery systems for precise CRISPR-Cas application. It also discusses the role of transcription factors in boosting enzyme production and the practical applications of these techniques for in-situ LCB biodegradation. Effective implementation of these advancements could foster a sustainable economy and mitigate the negative environmental impacts of current agricultural practices.
It is well recognized that carbon dioxide and air pollutants share similar emission sources so that synergetic policies on climate change mitigation and air pollution control can lead to remarkable co-benefits on greenhouse gas reduction, air quality improvement, and improved health. In the context of carbon peak, carbon neutrality, and clean air policies, this perspective tracks and analyzes the process of the synergetic governance of air pollution and climate change in China by developing and monitoring 18 indicators. The 18 indicators cover the following five aspects: air pollution and associated weather-climate conditions, progress in structural transition, sources, inks, and mitigation pathway of atmospheric composition, health impacts and benefits of coordinated control, and synergetic governance system and practices. By tracking the progress in each indicator, this perspective presents the major accomplishment of coordinated control, identifies the emerging challenges toward the synergetic governance, and provides policy recommendations for designing a synergetic roadmap of Carbon Neutrality and Clean Air for China.
Reducing wealth inequality and disparity is a global challenge. The economic system is mainly divided into (1) gift and reciprocity, (2) power and redistribution, (3) market exchange, and (4) mutual aid without reciprocal obligations. The current inequality stems from a capitalist economy consisting of (2) and (3). To sublimate (1), which is the human economy, to (4), the concept of a "mixbiotic society" has been proposed in the philosophical realm. This is a society in which free and diverse individuals, "I," mix with each other, recognize their respective "fundamental incapability" and sublimate them into "WE" solidarity. The economy in this society must have moral responsibility as a coadventurer and consideration for vulnerability to risk. Therefore, I focus on two factors of mind perception: moral responsibility and risk vulnerability, and propose a novel model of wealth distribution following an econophysical approach. Specifically, I developed a joint-venture model, a redistribution model in the joint-venture model, and a "WE economy" model. A simulation comparison of a combination of the joint ventures and redistribution with the WE economies reveals that WE economies are effective in reducing inequality and resilient in normalizing wealth distribution as advantages, and susceptible to free riders as disadvantages. However, this disadvantage can be compensated for by fostering consensus and fellowship, and by complementing it with joint ventures. This study essentially presents the effectiveness of moral responsibility, the complementarity between the WE economy and the joint economy, and the direction of the economy toward reducing inequality. Future challenges are to develop the WE economy model based on real economic analysis and psychology, as well as to promote WE economy fieldwork for worker coops and platform cooperatives to realize a desirable mixbiotic society.
Given the rising interest in the circular economy and blockchain hype, numerous integrations were proposed. However, studies on the practical feasibility were scarce, and the assumptions of blockchain potential in the circular economy were rarely questioned. With the help of eleven of the most prominent blockchain experts, the present study critically analyzed technology integration in many areas of the circular economy to forecast their possible outcomes. Delphi's technique is leveraged to reach a consensus among experts' visions and opinions. Results support the view that some circular economy integrations are unlikely to succeed, while others if specific conditions are met, may prove to be successful in the long run.
Artificial Intelligence (AI) can transform the knowledge economy by automating non-codifiable work. To analyze this transformation, we incorporate AI into an economy where humans form hierarchical organizations: Less knowledgeable individuals become "workers" doing routine work, while others become "solvers" handling exceptions. We model AI as a technology that converts computational resources into "AI agents" that operate autonomously (as co-workers and solvers/co-pilots) or non-autonomously (solely as co-pilots). Autonomous AI primarily benefits the most knowledgeable individuals; non-autonomous AI benefits the least knowledgeable. However, output is higher with autonomous AI. These findings reconcile contradictory empirical evidence and reveal tradeoffs when regulating AI autonomy.
Vladimir Beskorovainyi, Lyudmyla Kolesnyk, Mariia Alokhina
et al.
The subject of research in the article is the process of ranking objects in the lists of recommender systems. The goal of the work is to increase the efficiency of recommender systems by improving the method of determining preferences between objects in lists using the theory of multi-criteria decision-making. The following tasks are solved in the article: review and analysis of the current state of the problem of identifying advantages between objects and their ranking in the lists of recommender systems; analysis of filtering methods used in recommendation systems; decomposition of the decision support problem for selection of objects; development of a combined method for ranking objects in the lists of recommender systems, combining the procedures for selecting a subset of Pareto-optimal objects, structural-parametric synthesis of a scalar multi-criteria estimation model, and evaluating the entire set of selected objects. The following methods are used: mathematical modeling, systems theory, utility theory, decision theory, optimization and operations research. Results. Based on the results of the analysis of the modern methodology for ranking objects in the lists of recommendation systems, the possibility of increasing their efficiency has been established. To take into account factors difficult to formalize, the knowledge and experience of users, it is proposed to implement the determination of preferences between objects using the theory of multi-criteria decision making. The problem of forming lists of recommendation systems is decomposed into the tasks of selecting a subset of Pareto-optimal objects, structural-parametric synthesis of a scalar multi-criteria estimation model, and evaluating a set of selected objects. A combined method for ranking options has been developed that combines the procedures of ordinalistic and cardinalistic ordering technologies and allows one to correctly reduce the subsets of objects included in the lists of recommendations. Conclusions. The developed method for determining preferences expands the methodological foundations for automating the development and operation of recommendation systems, other multi-criteria decision support systems, allows for the correct reduction of the set of non-dominated objects for the final choice, taking into account factors that are difficult to formalize, knowledge and user experience. The practical use of the obtained results due to more economical method of forming lists when adding new objects will allow to decrease the time and capacity complexity of the procedures for providing recommendations, and due to taking into account of set of weighted local indexes and allocation of set of non-dominated objects - to increase quality of given recommendations.
The cross section is the basic data for building 3D geological models. It is inefficient to draw a large number of cross sections to build an accurate model. This paper reports the use of multi-source and heterogeneous geological data, such as geological maps, gravity and aeromagnetic data, by a conditional generative adversarial network (CGAN) and implements an intelligent generation method of cross sections to overcome the problem of inefficient modeling data based on CGAN. Intelligent generation of cross sections and 3D geological modeling are carried out in three different areas in Liaoning Province. The results show that: (a) the accuracy of the proposed method is higher than the GAN and Variational AutoEncoder (VAE) models, achieving 87%, 45% and 68%, respectively; (b) the 3D geological model constructed by the generated cross sections in our study is consistent with manual creation in terms of stratum continuity and thickness. This study suggests that the proposed method is significant for surmounting the difficulty in data processing involved in regional 3D geological modeling.
The information age is also an era of escalating social problems. The digital transformation of society and the economy is already underway in all countries, although the progress in this transformation can vary widely. There are more social innovation projects addressing global and local social problems in some countries than in others. This suggests that different levels of digital transformation might influence the social innovation potential. Using the International Digital Economy and Society Index and the Social Innovation Index, this study investigates how digital transformation of the economy and society affects the capacity for social innovation. A dataset of 29 countries was analysed using both simple and multiple linear regressions and Pearsons correlation. Based on the research findings, it can be concluded that the digital transformation of the economy and society has a significant positive impact on the capacity for social innovation. It was also found that the integration of digital technology plays a critical role in digital transformation. Therefore, the progress in digital transformation is beneficial to social innovation capacity. In line with the research findings, this study outlines the implications and possible directions for policy.
The gradual transition from fossil fuels to a carbon neutral economy is one of the greatest challenges of our time. The European Union has undertaken numerous initiatives aimed at what is called the energy – and at the same time digital – transition, in order to create growth, jobs, to improve the quality of life of citizens, and to fight climate change. The EU renewed its climate commitment by launching a regulatory process that led in 2019 to the final approval of a package of directives known as the “Clean Energy for all Europeans Package”1 aimed at ensuring a 40% reduction of greenhouse gas emissions compared to 1990 levels, a 32% increase in the use of renewable sources for final energy consumption, a 32,5% reduction in primary energy consumption compared to the trend scenario, an increase of 15% of cross-border electricity interconnection capacity on installed electricity generation capacity. In Italy, the Integrated National Plan for Energy and Climate 2021-2030, drawn up by the Ministry of Economic Development, the Ministry of the Environment, Land and Sea and the Ministry of Infrastructure and Transport, identifies objectives, trajectories and measures that represent our country’s commitment to achieving the European targets by 2030.
In this reference framework, the energy transition, that is the transformation of the electricity system, implies a series of challenges to be faced while maintaining the current high levels of service quality and avoiding an excessive increase in costs for the community.
Among the enabling factors of this transformation we can identify on the one hand the new digital technologies, which allow to collect information at low cost (IoT, smart meter), to transfer large data streams with reliable connectivity solutions (optical fiber, 5G) and to store and analyze data effectively (advanced analytics), on the other hand investments in innovation projects that bring together new digital solutions allowing to face the challenges of the energy context through a transition based on the integration of renewable sources, strengthening of transmission capacity, resilience of infrastructures. «We are witnessing a rethinking of the methods of managing networks, especially distribution networks, which must pass from passive to active. This direction of evolution is identified, at an international level, with the term Smart Grid2, implying highly innovative structures and operating methods that are also able to cope with the numerous problems related to the management of Diffused Generation, the promotion of energy efficiency and greater involvement of end users […]. Now it is no longer enough just to satisfy the growing demand for electricity, but we must respond to new needs that can only be solved thanks to the use of ICT» (Delfanti, 2011; Silvestri, 2011).
The current centralized and top-down distribution of energy will become more and more obsolete and will eventually disappear. In the new era, companies, administrations, homeowners will be able to become producers as much as consumers of their own energy (prosumers), the so-called “distributed generation”, by aggregating and collecting renewable energy generated locally and distributing it through smart grids (Mazzari, 2011).
Smart grids use wireless sensors, software and utility computing that allow to observe and control how much energy is consumed, to increase the generation and storage capacity of RES energy, to improve the quality and operational safety of the entire electricity distribution system, to allow the active participation of users in the market through the integration of all users connected to the grid.
In this regard, analyst Jesse Berst affirmed that smart meters can be considered as an invention equal to the telephone system, the transcontinental railway, the internet (Palma, 2011).
These preliminary considerations are useful for understanding the role played by COGEPA Telecommunication S.p.A., an engineering, design, construction and maintenance company of telecommunications networks, technological systems, networking and low, medium and high voltage energy transport systems. The number of the Address Book has identified the Company as a qualified interlocutor whose reference market is represented by telephone operators, large infrastructures and Public Administrations.
In the following pages, through a dialogue with Eng. Luca Palermo, Commercial Director of COGEPA Telecommunication S.p.A., we will develop some reasoning on Smart Grids and the role of digital technologies, and on how the company’s know-how has allowed to anticipate the opportunities offered by technological innovations while respecting the environment in an energy saving and efficiency optics.
In questo quadro di riferimento la transizione energetica, nella fattispecie la trasformazione del sistema elettrico, implica una serie di sfide da affrontare mantenendo gli attuali elevati livelli di qualità del servizio ed evitando un aumento eccessivo dei costi per la collettività.
Tra i fattori abilitanti di questa trasformazione si possono individuare da un lato le nuove tecnologie digitali, che consentono di raccogliere informazioni a basso costo (IoT, smart meter), di trasferire grandi flussi di dati con soluzioni affidabili di connettività (fibra ottica, 5G) e di stoccare e analizzare i dati in maniera efficace (advanced analytics), dall’altro gli investimenti in progetti di innovazione che mettono insieme le nuove soluzioni digitali permettendo di affrontare le sfide del contesto energetico attraverso una transizione basata sull’integrazione delle fonti rinnovabili, il rafforzamento della capacità di trasmissione, la resilienza delle infrastrutture. «Si assiste a un ripensamento delle modalità di gestione delle reti, soprattutto di distribuzione, che devono passare da passive ad attive. Questa direzione di evoluzione è identificata, a livello internazionale, con il termine smart grid2, sottintendendo strutture e modalità operative fortemente innovative che siano anche in grado di far fronte ai numerosi problemi legati alla gestione della Generazione Diffusa, alla promozione della efficienza energetica e a un maggiore coinvolgimento degli utenti finali […]. Adesso non basta più solo soddisfare la crescente domanda di energia elettrica ma bisogna rispondere a nuove esigenze risolvibili solo grazie al ricorso alle ICT» (Delfanti, 2011; Silvestri, 2011).
L’attuale distribuzione centralizzata e dall’alto verso il basso di energia, diverrà sempre più obsoleta fino a scomparire. Nella nuova era le aziende, le Amministrazioni, i proprietari di casa potranno diventare produttori tanto quanto consumatori della loro stessa energia, la cosiddetta “generazione distribuita”, aggregando e raccogliendo l’energia rinnovabile generata localmente e distribuendola per mezzo delle smart grid (Mazzari, 2011).
Le reti intelligenti utilizzano sensori wireless, software e utility computing che permettono di osservare e controllare quanta energia viene consumata, di aumentare la capacità di generazione e stoccaggio dell’energia da FER, di migliorare la qualità e la sicurezza di funzionamento dell’intero sistema di distribuzione di energia elettrica, di consentire la partecipazione attiva dell’utenza nel mercato attraverso l’integrazione di tutti gli attori connessi alla rete.
A tal proposito l’analista Jesse Berst ha affermato, riguardo ai contatori intelligenti, che possono essere considerati come un’invenzione pari al sistema telefonico, alla ferrovia transcontinentale, ad internet (Palma, 2011).
Aesthetics of cities. City planning and beautifying, Architectural drawing and design