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Grid connected energy storage systems are regarded as promising solutions for providing ancillary services to electricity networks and to play an important role in the development of smart grids. Thus far, the more mature battery technologies have been installed in pilot projects and studies have indicated their main advantages and shortcomings. The main concerns for wide adoption are the overall cost, the limited number of charging cycles (or lifetime), the depth of discharge, the low energy density and the sustainability of materials used. Vanadium Redox Flow Batteries (VRFB) are a promising option to mitigate many of these shortcomings, and demonstration projects using this technology are being implemented both in Europe and in the USA. This study presents a model using MATLAB/Simulink, to demonstrate how a VRFB based storage device can provide multi-ancillary services, focusing on frequency regulation and peak-shaving functions. The study presents a storage system at a medium voltage substation and considers a small grid load profile, originating from a residential neighbourhood and fast charging stations demand. The model also includes an inverter controller that provides a net power output from the battery system, in order to offer both services simultaneously. Simulation results show that the VRFB storage device can regulate frequency effectively due to its fast response time, while still performing peak-shaving services. VRFB potential in grid connected systems is discussed to increase awareness of decision makers, while identifying the main challenges for wider implementation of storage systems, particularly related to market structure and standardisation requirements.

Industry 4.0 concepts and technologies ensure the ongoing development of micro- and macro-economic entities by focusing on the principles of interconnectivity, digitalization, and automation. In this context, artificial intelligence is seen as one of the major enablers for Smart Logistics and Smart Production initiatives. This paper systematically analyzes the scientific literature on artificial intelligence, machine learning, and deep learning in the context of Smart Logistics management in industrial enterprises. Furthermore, based on the results of the systematic literature review, the authors present a conceptual framework, which provides fruitful implications based on recent research findings and insights to be used for directing and starting future research initiatives in the field of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in Smart Logistics.

This paper analyses the role of barriers preventing the worldwide take-up of a clean technology: the electrical arc furnace. It also identifies which barriers affect a parameter that summarises the combined effect of all of them. The first step, determination of the combined effect of the barriers, is carried out using a novel approach to model the diffusion of innovations. This new approach is composed only by terms that account for the driver of innovations and the parameter that summarises the effect of barriers. The objective quantification of the effect of barriers in the diffusion of innovations opens up new opportunities for designing policies to overcome the barriers identified as the most relevant, for identifying the effect of existing policies, for relating innovation indicators with those barriers or for better incorporating the effect of barriers in bottom-up models that forecast the technological evolution of the economy.

Future power systems require a change from a "vertical" to a "horizontal" structure, in which the customer plays a central role. As buildings represent a substantial aggregation of energy consumption, the intertwined operation of the future power grid and the built environment is crucial to achieve energy efficiency and sustainable goals. This transition towards a so-called smart grid (SG) requires advanced building energy management systems (BEMS) to cope with the highly complex interaction between two environments. This paper proposes an agent-based approach to optimize the inter-operation of the SG–BEMS framework. Furthermore a computational intelligence technique, i.e. Particle Swarm Optimization (PSO), is used to maximize both comfort and energy efficiency. Numerical results from an integrated simulation show that the operation of the building can be dynamically changed to support the voltage control of the local power grid, without jeopardizing the building main function, i.e. comfort provision.

In this paper we study to what extent electric propulsion is disrupting ‘the order’ in the automotive industry with six extensions to Christensen's notion of disruptive innovation (1997). For decades the automotive sector has relied on the internal combustion engine (ICE) as the established propulsion technology, but due to environmental regulation and geo-political scarcity problems associated with fossil fuel use, electric propulsion is increasingly applied as sole or additional power source. We elaborate the Christensen typology, rooted in industrial analysis, with a regime evolution framework based on changes in technology and the institutional context of production and use, with special attention to consumer perspectives and government regulation. We offer a hypothesis for structural conditions for market disruption and test this hypothesis against the development trajectory of full-electric vehicles (FEV). Drawing on evidence from a range of recent FEV studies, our analysis suggests that the disruptive niche of full-electric mobility is currently insufficient to displace the ICE regime.

According to the European Commission, green infrastructure (GI) is conceived as a strategically planned network of natural and semi-natural areas. This definition highlights three important issues: environment protection, ecosystems multifunctionality and ecological connectivity. Building upon a methodology that identifies a Sardinian regional GI in relation to four values (conservation value, natural value, recreational value and landscape value), this study aims at assessing the suitability of areas situated within and outside protected areas’ boundaries to be included in the above-mentioned Sardinian regional GI. In relation to conservation value, outcomes reveal a higher suitability of patches situated within protected areas to be included in the regional GI, whereas in relation to the other three values, the behavioral patterns are less evident. These results suggest specific policy recommendations such as mitigation of land-taking processes, increase of Natura 2000 Network’s size, accurate identification of landscape goods, and improved accessibility to sites characterized by outstanding natural beauty.

Abstract The rapidly growing construction industry has accelerated water and energy scarcity in China, threatening its sustainable development. This study integrates multi-regional input-output (MRIO) and data envelopment analysis (DEA) to investigate the water-energy nexus in the construction industry at the provincial level through the entire industrial supply chain. Results show that the construction industry accounts for 8.97% and 27.20% of virtual water and embodied energy in China, respectively. The western area experiences the most energy- and water-intensive construction processes given its backward economy and outdated technological development. The northern area faces great challenges with regard to energy intensity improvements, whereas the central regions suffer from large pressure relating to inefficient water use. The manufacture of non-metallic mineral products, smelting, and the pressing of metals are the largest suppliers of virtual water and embodied energy. The efficiency assessment results demonstrate that Jiangsu and Zhejiang are two DEA-effective regions. China has achieved a relatively high level of scale efficiency but suffers from backward technology.

Positive Energy Districts (PED) are areas within cities that generate more renewable energy than they consume, contributing to cities’ energy system transformation toward carbon neutrality. Since PED is a novel concept, the implementation is very challenging. Within the European Cooperation in Science and Technology (COST) Action, which offers an open space for collaboration among scientists across Europe (and beyond), this paper asks what the needs for supporting the implementation of PEDs are. To answer this, it draws on Delphi process (expert reviews) as the main method alongside the literature review and also uses surveys as supplementary methods to identify the main challenges for developing PEDs. Initial findings reveal seven interacting topics that later were ranked as highest to the lowest as the following: governance, incentive, social, process, market, technology and context. These are interrelated and interdependent, implying that none can be considered in isolation of the others and cannot be left out in order to ensure the successful development of PEDs. The resources that are needed to address these challenges are a common need for systematic understanding of the processes behind them, as well as cross-disciplinary models and protocols to manage the complexity of developing PEDs. The results can be the basis for devising the conceptual framework on the development of new PED guides and tools.