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Clean-tech industry, also identified as the advanced stage of eco-friendly industry and typical high-tech, has to gain enough financial support to develop. Meanwhile, it has provided substantial market opportunities as well as investment options for venture capitalist. In an attempt to form a comprehensive understanding of VC policy preferences in clean-tech sector, relevant studies have been done by researchers from organizations, universities, and firms, from various perspectives, forming an independent “niche” research field. Based on the interactive relation between industry development and clean-tech investing, this paper performs a time-sharing, multidimensional, and dynamic review on international clean-tech policies literatures, focusing primarily on investor preferences. We aim to provide references to the lenses and methods currently used for future research including policy tool design, investment attraction effects, and policy efficiency. Moreover, the findings facilitate the innovation and expansion of related research for China, by which it can form effective financial support for domestic clean-tech industry.

AbstractConcrete is a valuable construction material with high mechanical strength and durability, used extensively in the construction industry. It is produced by mixing sand, stones, cement, and water in different proportions depending on the desired quality of the final product. Water reducers are additional chemical ingredients used in concrete to reduce the quantity of water required in the concrete mixture. When added to concrete, water reducers increase the workability and flowability of concrete in the freshly mixed state and improve the mechanical strength and durability of the final hardened product. This review paper describes the different types and applications of concrete water reducers used in the construction industry including their working mechanisms and fluidity effects on concrete properties. It discusses the production of synthetic and bio‐based concrete water reducers and reviews the present challenges involved in the preparation of bio‐based concrete water reducers from renewable resources. © 2023 Society of Industrial Chemistry and John Wiley & Sons Ltd.

Abstract The increased environmental awareness coupled with the recent changes in the oil prices triggered the necessity of focusing on effective management of energy systems. Global climate change has caused many people to consider ways of reducing greenhouse gases Renewable energy has become an essential feature in curtailing emission of Green House Gases, while meeting the demand for energy. This paper presents an innovation system framework for development and diffusion of renewable energy technologies. The framework is used to identify opportunities for small and medium enterprises in the renewable energy sector. A case study on a successful development, installation and implementation of solar thermal systems households in Calgary, Alberta, by an entrepreneurial firm, is also presented.

In response to increasing needs and new opportunities, a provincewide network of Quebec institutions of higher learning, R&D agencies, and technology users has been set up to carry out research in aluminum production, fabrication, and transformation technologies, and to help channel the expertise and resources into large projects with more efficiency in terms of generating synergy and funding, cutting-edge research, qualified personnel training, and technology transfers. It is a unique experience in collaborative, multipartite, university-based R&D which, in addition to a comprehensive, far-reaching research program, emphasizes the various aspects of dispersed inter-institutional coordination, resource sharing, synergy creation, joint graduate training, diversification of funding, and intellectual property.

With the prevalence of smart appliances, smart meters, and Internet of Things (IoT) devices in smart grids, artificial intelligence (AI) built on the rich IoT big data enables various energy data analysis applications and brings intelligent and personalized energy services for users. In conventional AI of Things (AIoT) paradigms, a wealth of individual energy data distributed across users’ IoT devices needs to be migrated to a central storage (e.g., cloud or edge device) for knowledge extraction, which may impose severe privacy violation and data misuse risks. Federated learning, as an appealing privacy-preserving AI paradigm, enables energy data owners (EDOs) to cooperatively train a shared AI model without revealing the local energy data. Nevertheless, potential security and efficiency concerns still impede the deployment of federated-learning-based AIoT services in smart grids due to the low-quality shared local models, non-independently and identically distributed (non-IID) data distributions, and unpredictable communication delays. In this article, we propose a secure and efficient federated-learning-enabled AIoT scheme for private energy data sharing in smart grids with edge-cloud collaboration. Specifically, we first introduce an edge-cloud-assisted federated learning framework for communication-efficient and privacy-preserving energy data sharing of users in smart grids. Then, by considering non-IID effects, we design a local data evaluation mechanism in federated learning and formulate two optimization problems for EDOs and energy service providers. Furthermore, due to the lack of knowledge of multidimensional user private information in practical scenarios, a two-layer deep reinforcement-learning-based incentive algorithm is developed to promote EDOs’ participation and high-quality model contribution. Extensive simulation results show that the proposed scheme can effectively stimulate EDOs to share high-quality local model updates and improve the communication efficiency.

Abstract In this research, an approach for selling price determination by electricity retailer in smart grid has been proposed with considering uncertainty. Also, hydrogen storage system (HSS) containing electrolyser (EL), hydrogen storage tanks (HST) and fuel cell (FC) are used as energy storage system (ESS) in smart grid. Also, demand response program as virtual generation units is proposed to increase retailer profit. Furthermore, renewable energy sources, i.e., photovoltaic (PV) system and wind turbine (WT), as well as power market (PM), bilateral contracts (BCs) and distributed generation (DG) units are used as multiple energy procurement sources. All uncertain parameters are modelled using the scenario-based stochastic framework. In the proposed model, the selling price is determined based on real-time pricing (RTP) by the retailer and it is compared with time-of-use pricing (TOU) in the smart grid. The selling price determination based on RTP and demand response program implementation increases the expected profit of retailer. Four case studies are investigated to validate the proposed model.

Smart grid, as one of the most critical infrastructures, is vulnerable to a wide variety of cyber and/or physical attacks. Recently, a new category of threats to smart grid, named coordinated cyber-physical attacks (CCPAs), are emerging. A key feature of CCPAs is to leverage cyber attacks to mask physical attacks which can cause power outages and potentially trigger cascading failures. In this paper, we investigate CCPAs in smart grid and show that an adversary can carefully synthesize a false data injection attack vector based on phasor measurement unit (PMU) measurements to neutralize the impact of physical attack vector, such that CCPAs could circumvent bad data detection without being detected. Specifically, we present two potential CCPAs, namely replay and optimized CCPAs, respectively, and analyze the adversary’s required capability to construct them. Based on the analytical results, countermeasures are proposed to detect the two kinds of CCPAs, through known-secure PMU measurement verification (in the cyber space) and online tracking of the power system equivalent impedance (in the physical space), respectively. The implementation of CCPAs in smart grid and the effectiveness of countermeasures are demonstrated by using an illustrative 4-bus power system and the IEEE 9-bus, 14-bus, 30-bus, 118-bus, and 300-bus test power systems.

Smart grid has emerged as the next generation of power grid, due to its reliability, flexibility, and efficiency. However, smart grid faces some critical security challenges such as the message injection attack and the replay attack. If these challenges cannot be properly addressed, an adversary can maliciously launch the injected or replayed message attacks to degrade the performance of smart grid. To cope with these challenging issues, in this paper, we propose an efficient authentication scheme that employs the Merkle hash tree technique to secure smart gird communication. Specifically, the proposed authentication scheme considers the smart meters with computation-constrained resources and puts the minimum computation overhead on them. Detailed security analysis indicates its security strength, namely, resilience to the replay attack, the message injection attack, the message analysis attack, and the message modification attack. In addition, extensive performance evaluation demonstrates its efficiency in terms of computation complexity and communication overhead.

Smart grid, as the next generation of power grid characterized by “two-way” communications, has been paid great attention to realizing green, reliable, and efficient electricity delivery for our future lives. In order to support the two-way communications in smart grid, a large number of smart meters (SMs) should be deployed to customers to report their near real-time data to control center for monitoring purpose. However, this kind of real-time report could disclose users’ privacy, bringing down the users’ willingness to participate in smart grid. In order to address the challenge, in this paper, by considering the lifetime of SMs as exponential distribution, we propose a diverse grouping-based aggregation protocol with error detection (DG-APED), which employs differential privacy technique into grouping-based private stream aggregation for secure smart grid communications. DG-APED can not only achieve privacy-preserving aggregation, but also perform error detection efficiently when some SMs are malfunctioning. Detailed security analysis shows that DG-APED can guarantee the security and privacy requirements of smart grid communications. In addition, extensive performance evaluation also verifies the effectiveness and efficiency of the proposed DG-APED.