IET Smart Cities最新文献

The increase in the ageing of the population calls for advanced monitoring and actuation systems for the smart control and management of elderly households that should manage to optimise multi-objective control problems. This study proposes a state-of-art approach, based on Approximate Dynamic Programming, that tackles multiple challenges, aiming to maximise the energy efficiency, the electricity cost reduction, the user comfort, and the load monitoring of a typical Greek residency (simulated in EnergyPlus), including both controllable and uncontrollable loads, an energy storage system, and power generation units. The article presents the various simulated loads, pricing schemes under Greek regulations and variety of control strategies that are co-ordinately used to minimise the defined objective functions in contrast to the current state-of-the-art approaches. The latter either focus on single objective problems or use simpler iterative and often limited to specific variables, approaches for multi-objective control. Finally, the presented results provide insights for the optimal and safe residential management and validate the performance of the proposed control strategy in comparison with alternative demand response strategies, offering improved performance by 20% in terms of energy efficiency, cost reduction, and thermal comfort.

Electric vehicles (EVs) are seen as a solution to reduce transport-related greenhouse gas emissions. A major obstacle to wider adoption is the insufficient amount of charging stations. Furthermore, supplying charging stations with renewable energy is still in its infancy. The selection of optimal locations for charging stations is important to best serve the users and maximise the possibilities of renewable energy use. Given this background, this study developed an approach for Solar-supplied Electric Vehicle Charging Station (EVCS) location selection by combining EVCS and solar farm site selection studies using Geographical Information System (GIS) and Analytic Hierarchy Process (AHP). The study determined the most important criteria for site selection based on previous solar and EVCS site selection studies and expert opinions. The 10 most important criteria according to the survey results were: availability of power, solar energy potential, solar panel installation cost, number of EVs, operation and management costs, land cost, distance from roads/highways, distance from current EVCSs, industrial capability of installation and distance to high population density centres. The importance weights of these criteria were assigned using AHP method. The findings are expected to benefit urban planners, decision-makers, and researchers designing solar-supplied EV charging infrastructure.

Three-dimensional Geographic Information System (3D GIS) technology is often used in geology, mining, hydrology and other fields. It is also used in intelligent management of urban comprehensive pipe gallery projects, because the real environment with the sky, ground, and underground can be intuitively simulated in the virtual reality environment by 3D GIS platform. Through the research on data integration and application technology of urban comprehensive pipe gallery, the 3D model of comprehensive pipe gallery project data can be created based on Building Information Modelling and GIS technologies, and a comprehensive underground pipelines network management information system has been developed to realise the integration of various data, including 3D data of comprehensive pipe gallery model and underground pipe network model, 2D or 3D GIS data of the ground and underground etc. On this basis, the functions of intelligent planning and approval management, the safety analysis and disaster prevention planning and decision-making of urban comprehensive pipe gallery projects can be further realised. The research results have been applied to the planning and construction process management of the comprehensive pipe gallery project in the sub-centre of Beijing.

Architectural strategy in the conservation field should be on both levels, tangible and intangible. The conservation of the non-tangible part includes signs and symbols. It transmits in various ways, like oral, artistic, and literary forms of expression. The visitor of historical places can participate in non-tangible conservation if the architect makes the visitor attracted to the sites. To make the visitor see the values of the sites, the visitor should move to different parts in different directions instead of seeing a few buildings and monuments. The study aims to increase visitor activity to promote non-physical conservation. Many places like Koya city have a very high architectural value but it is still neglected and uncovered by visitors. Researchers made observations to demonstrate the general characteristics of the district, then designed a questionnaire to collect the required data, specifying the locations they mostly visit, determining the paths they have taken, defining the areas visitors rarely visit, and alleys they do not go through for various reasons, gradually becoming a neglected place. Finally, the study conducted space syntax analysis based on pedestrian movement to reshape the visitor's movement. Based on the findings, the study will design strategies to enhance tourist movement and conserve non-tangibles.

Promoting the intelligent level of urban pipelines network planning and management is an important way to improve the rational development and utilisation of urban underground space and ensure the safe operation of urban infrastructure systems. The research on using the rapid and visual expression technology of 3D pipelines network model data, which has the functions of techno-economic indexes calculation and automatic comparison of underground pipeline construction projects, effectively avoids possible errors in manual calculation and comparison and improves the efficiency and accuracy of the digital declaration process for project planning. In the 3D Geographic Information Systems (GIS) platform environment, this research help to develop many analysis and approval functions based on the data of the underground pipelines network model and the integrated pipes gallery Building Information Modelling, such as evaluating the compliance, feasibility and scientific of the planning scheme of the underground pipeline construction project, comparing the completion data with the data approved by the administrative departments of the project, evaluating the consistency of them and judging the compliance of the project completion data. The application results of some actual projects cases show that this research can improve the efficiency of planning declaration and approval analysis.

One of the most significant problems of the modern electricity markets is to deal with renewable energy resources (RERs) scheduling. The RER generations face severe stochastic behaviour, such that short-term scheduling of them is also complicated. To overcome this drawback, using hydro pumped storage units (HPSUs) as a fast response and eco-friendly technology can help to smooth fluctuations of these types of generations and consequently to appropriately dispatch all generations in the energy and reserve market. This article suggests a stochastic optimisation model to optimally operate thermal power plants as well as HPSUs in the day ahead energy and reserve market. Optimisation aims to minimise operation costs, emissions, and social costs subject to several technical constraints. There is an intrinsic deviation between predicted and actual uncertainty variables in the power system. This article presents a stochastic optimal operation model based on robust optimisation. To improve the flexibility of the proposed market, the curtailed demand as a demand response programme (DRP) is taken into consideration. The CPLEX solver of the GAMS software is used to solve the proposed model which has been formulated as a robust mixed integer linear problem (RMILP). The effectiveness of the proposed model is evaluated by applying the offered model to the 9-bus test power system.

Fast-growing urban areas bring challenges to government authorities. The urban planning system in Australia is still very analogue with paper-based systems as standard practice. Two-dimensional document-based blueprints and Geographic Information System (GIS) maps do not always give stakeholders and investors the necessary insights. By exploring Mixed Reality (MR) application at the municipal government level, this research explains how metaverse-related immersive technologies can assist the government in better engaging with stakeholders and potential partners to improve their understanding of the urban environment and showcase future city developments to foster new opportunities and contribute to the development of the real living world.

Local energy self-sufficiency, in which the supply and demand of electricity are controlled such that the generated power from distributed energy resources (DERs) is consumed locally based on a power supply-and-demand forecast, mitigates the burden on the power system and contributes to the efficient use of DERs in smart cities. However, widely available smart metres cannot measure behind-the-metre pure demand and generation from prosumers. Pure demand and generation forecasts without additional metering contribute to advanced supply-and-demand control in smart cities, including demand response. This study proposes a method of forecasting spatio-temporal behaviours of behind-the-metre pure demand and generation by focussing on the information of net demand distribution observable from the smart metres; the proposed method initially predicts the spatio-temporal net demand distribution with a combined forecaster based on the persistence and non-parametric regression models, and then separately estimates the behind-the-metre pure demand and generation by using demand forecast result of neighbouring pure-consumers extracted by considering the area-scale behaviours of the smart metering data. The simulation results demonstrate that the proposed method provides accuracy comparable to forecasts conducted by directly measuring pure demand and generation, without requiring the installation of additional metres.

The reduction of adverse environmental effects and the socioeconomic advantages of renewable energy systems promote greater integration of distributed energy systems into the traditional electrical networks. A new type of sharing economy is emerging with the sharing of energy resources to reduce transaction costs by using platform services in the cloud. Given the obstacles posed by the legacy system and various forms of renewable energy integration, Distributed Energy and Micro Grids (DE-MG) are an efficient means of raising the quality of energy services. Rules for microgrid scalability, maintaining a budget, and security can make this difficult. Consumers are better at receiving the best renewable energy allotment price using a cloud-based Peer-to-Peer (P2P) network. The main objective is to lower the daily energy cost of microgrid power in commercial buildings. In the proposed work, cloud-based P2P for peer-Multi Agent System (p-MAS) optimization techniques are used to reduce system peak and integrated Demand Response (DR) with Energy Management System (EMS) in a commercial MG. To fill knowledge gaps about how various power market architectures and individual decision-making processes impact local interactions and market outcomes, cloud-based P2P for Modelling Leveraging Agents (MLA) is used for bill calculation. A performance measure is finally created for cost evaluation and reliability to measure the social benefits of cloud-based P2P models for exchanging energy. For various price environments and resource types, a comparison between the proposed cloud-based P2P model with an existing P2P model for exchanging energy is provided. The primary use of a distributed P2P model for exchanging power in a microgrid is to reduce electricity costs and increase grid environment reliability.

The smart city model for operation and governance of modern cities requires huge quantities of data about people and the environment to support related applications, and crowdsourcing is effective for collecting and processing such data. Numerous reviews of the literature on crowdsourcing and smart cities as separate research fields have been published. Research on the intersection of the two is relatively new and lacks a systematic literature review. This study applies bibliometric and scientometric research methods and the research tools to study 367 related publications retrieved from the Web of Science database. Keyword co-occurrence analysis was conducted to find the distribution of publications, research cooperation, and major research areas. Document co-citation analysis was used to examine the domain knowledge structure, and citation burst detection was used to identify trends in research in this topic area. The analysis results clarify the research content and evolutionary context and reveal emerging research trends.