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The paper presents new strategies and algorithms for future mobile power infrastructure planning and operational management in smart cities. The efforts have been made to develop a resilient Electric Vehicle (EV) infrastructure for smart city applications. The goal of this work is to maximize the profit of utility and EV owners participating in rea...
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co-simulation of optimal electric vehicle supply equipment (EVSE) and techno-economic system design
models to investigate the behaviors of various...
Citations
... Mobile power infrastructure planning (electric vehicle) [143]. QS/OF. ...
This literature review analyzes and classifies methodological contributions that answer the different challenges faced by smart cities. This study identifies city services that require the use of artificial intelligence (AI); which they refer to as areas of application of A. These areas are classified and evaluated, taking into account the five proposed domains (government, environment, urban settlements, social assistance, and economy). In this review, 168 relevant studies were identified that make methodological contributions to the development of smart cities and 66 areas of application of AI, along with the main challenges associated with their implementation. The review methodology was content analysis of scientific literature published between 2013 and 2020. The basic terminology of this study corresponds to AI, the internet of things, and smart cities. In total, 196 references were used. Finally, the methodologies that propose optimization frameworks and analytical frameworks, the type of conceptual research, the literature published in 2018, the urban settlement macro-categories, and the group city monitoring-smart electric grid, make the greater contributions.
... Moreover, the recent advancements in small or medium-sized power generation and clean renewable power generation technologies have led to the large-scale integration of distributed energy resources (DERs), distributed generation (DG) and microgrids in power distribution systems. The paradigm shift of the modern power industry has established a strong link between green and smart city development and renewable energy sources [6]. This can be found in a European green city index report considering the factors such as CO 2 emission and strategy to reduce it, air quality, and percentage of renewable share in current power generation [7]. ...
In this paper, a new optimization framework is developed for optimal planning of low-carbon microgrids for clean city applications. It models the effect of recent carbon policies imposed by the environmental protection agencies on the power distribution utilities especially, in urban areas. To mimic the various techno-economic and social aspects of emerging dispatchable distributed generation technologies in the proposed framework, a new objective function is introduced. The moth search optimization technique is adopted to solve the problem aiming to determine the optimal mix of energy generation. Different scenarios are framed and investigated on a benchmark test distribution system of 33 buses. The simulation results are found to be inspiring in quantifying the optimal carbon cap/tax, whilst improving the system performance.
... journal articles that were peer-reviewed, and available online. The articles were then 'eye-balled' to ensure they were consistent with the keyword search, the abstracts assessed Probabilistic methods [70]; [73][74][75]; [77]; [80][81][82]; [87]; [90]; [93,94]; [96][97][98][99][100][101]; [112][113][114]; [134]; [136][137][138] Knowledge-based [26]; [63]; [67-71]; [73]; [77,78]; [82]; [92]; [98]; [100]; [112]; [136]; [139][140][141][142][143] Search and optimization [26]; [67-69]; [77]; [79,80]; [82]; [86]; [92]; [98]; [101]; [106]; [112]; [116]; [128]; [130]; [134]; [144][145][146] Logic-based [63]; [67,68]; [69]; [71]; [73]; [77,78]; [82]; [92]; [98]; [100]; [112]; [136]; [140][141][142][143] Embodied intelligence [26]; [70]; [73]; [75,76]; [80]; [82]; [98]; [104]; [116]; [128]; [134]; [137]; [147] AI applications Neural networks [26]; [63]; [67-70]; [71]; [73]; [75]; [77][78][79][80][81][82][83][84][85]; [87]; [89,90]; [92]; [95]; [97][98][99][100][101][102][103]; [105][106][107]; [109][110][111][112]; [116][117][118][119][120][121][122][123][124][125][126]; [128][129][130][131][132][133][134] Evolutionary algorithms [26]; [67-69]; [77]; [79][80]; [82]; [86]; [92]; [98]; [101]; [106]; [112]; [116]; [128]; [130]; [134]; [144][145][146] Expert systems [26]; [63]; [67-70]; [71]; [73]; [77,78]; [82]; [92]; [98]; [100]; [112]; [136]; [140][141][142][143] Distributed artificial intelligence [26]; [70]; [73]; [75,76]; [80]; [82]; [92]; [98]; [116]; [128]; [134]; [137]; [147,148] Computer vision [73][74][75]; [87]; [93,94]; [96]; [99]; [101]; [113,114]; [118]; [138] Decision networks [70]; [77]; [80]; [82]; [90]; [98]; [112]; [134]; [136] ...
... journal articles that were peer-reviewed, and available online. The articles were then 'eye-balled' to ensure they were consistent with the keyword search, the abstracts assessed Probabilistic methods [70]; [73][74][75]; [77]; [80][81][82]; [87]; [90]; [93,94]; [96][97][98][99][100][101]; [112][113][114]; [134]; [136][137][138] Knowledge-based [26]; [63]; [67-71]; [73]; [77,78]; [82]; [92]; [98]; [100]; [112]; [136]; [139][140][141][142][143] Search and optimization [26]; [67-69]; [77]; [79,80]; [82]; [86]; [92]; [98]; [101]; [106]; [112]; [116]; [128]; [130]; [134]; [144][145][146] Logic-based [63]; [67,68]; [69]; [71]; [73]; [77,78]; [82]; [92]; [98]; [100]; [112]; [136]; [140][141][142][143] Embodied intelligence [26]; [70]; [73]; [75,76]; [80]; [82]; [98]; [104]; [116]; [128]; [134]; [137]; [147] AI applications Neural networks [26]; [63]; [67-70]; [71]; [73]; [75]; [77][78][79][80][81][82][83][84][85]; [87]; [89,90]; [92]; [95]; [97][98][99][100][101][102][103]; [105][106][107]; [109][110][111][112]; [116][117][118][119][120][121][122][123][124][125][126]; [128][129][130][131][132][133][134] Evolutionary algorithms [26]; [67-69]; [77]; [79][80]; [82]; [86]; [92]; [98]; [101]; [106]; [112]; [116]; [128]; [130]; [134]; [144][145][146] Expert systems [26]; [63]; [67-70]; [71]; [73]; [77,78]; [82]; [92]; [98]; [100]; [112]; [136]; [140][141][142][143] Distributed artificial intelligence [26]; [70]; [73]; [75,76]; [80]; [82]; [92]; [98]; [116]; [128]; [134]; [137]; [147,148] Computer vision [73][74][75]; [87]; [93,94]; [96]; [99]; [101]; [113,114]; [118]; [138] Decision networks [70]; [77]; [80]; [82]; [90]; [98]; [112]; [134]; [136] ...
... Smart grid systems, integrated with AI technology, can be used to control power systems and optimize energy consumption [78,79]. Including the planning and management of electric vehicle charging [145], public lighting [75], and data [121]. AI can also assist with the distribution of renewable electricity generated from multiple, often non-traditional sources-including body heat [125]-, the identification of inefficiencies, and future forecasting [134,157]. ...
Artificial intelligence (AI) is one of the most disruptive technologies of our time. Interest in the use of AI for urban innovation continues to grow. Particularly, the rise of smart cities—urban locations that are enabled by community, technology, and policy to deliver productivity, innovation, livability, wellbeing, sustainability, accessibility, good governance, and good planning—has increased the demand for AI-enabled innovations. There is, nevertheless, no scholarly work that provides a comprehensive review on the topic. This paper generates insights into how AI can contribute to the development of smarter cities. A systematic review of the literature is selected as the methodologic approach. Results are categorized under the main smart city development dimensions, i.e., economy, society, environment, and governance. The findings of the systematic review containing 93 articles disclose that: (a) AI in the context of smart cities is an emerging field of research and practice. (b) The central focus of the literature is on AI technologies, algorithms, and their current and prospective applications. (c) AI applications in the context of smart cities mainly concentrate on business efficiency, data analytics, education, energy, environmental sustainability, health, land use, security, transport, and urban management areas. (d) There is limited scholarly research investigating the risks of wider AI utilization. (e) Upcoming disruptions of AI in cities and societies have not been adequately examined. Current and potential contributions of AI to the development of smarter cities are outlined in this paper to inform scholars of prospective areas for further research.
... The dispatchable DERs may include combined heat and power (CHP), biomass, fuel cells, battery energy storage systems (BESSs), diesel engines, shunt capacitors etc. In the near future, the electric vehicles can also be considered as one of the promising DERs to feed power back to microgrids when needed [28]. The CHP technology can save millions by utilizing the waste energy produced during electricity production. ...
In this article, a new business model considering multiple stakeholders is proposed to develop a framework for third-party investment and future flexible retail electricity market in community microgrids. The proposed two-stage optimisation platform generates opportunities for multiple stakeholders to invest in the design of community microgrids, comprising multiple and different distributed energy resources such as renewable generation units, battery energy storage systems, and micro diesel engines, to minimize daily operational costs of the system. To proliferate the prosumers in retail energy markets as per the Office of Gas and Electricity Markets, United Kingdom, a peer-to-peer energy trading and energy management scheme is also proposed. The optimal sizing of urban and remote community microgrids are determined in stage-1, followed by their optimal operations to minimise the daily operating cost of the community system in stage-2. An improved version of the genetic algorithm is employed to optimise decision variables in both the stages. Different cases are investigated which show the tremendous potential of revenue generation for all stakeholders while effectively optimizing techno-economic operations of microgrids.
... In order to solve the proposed optimization framework, developed in Section 2, a powerful optimization method is required therefore genetic algorithm (GA) is adopted. It has strong exploration ability to search the global optimal solution for real-life engineering optimization problems [2,14,15,16]. The optimization variables considered in this model are optimized in each hour. ...
In this article, a new business model comprising multiple stakeholders is proposed to develop a frame for future flexible retail energy market in community microgrids. The microgrid comprises multiple and different distributed energy resources (DERs) such as renewable generation units, battery energy storage systems (BESSs), and micro diesel engines (MDE), to minimize daily operational costs of the system. To solve the defined complex optimization model, some operational strategies are proposed and then genetic algorithm is adopted to determine the hourly optimal power dispatch. The case study shows that the proposed model minimizes the daily operating cost of the community system effectively.
In recent years, enormous amounts of digital data have been generated. In parallel, data collection, storage, and analysis technologies have developed. Recently, there has been an increasing trend of people moving towards urban areas. By 2030 more than 60% of the world's population will live in an urban environment. Urban areas are big data resource because they include millions of citizens, technological devices, and vehicles which generate data continuously. Besides, rapid urbanization brings many challenges, such as environmental pollution, traffic congestion, health problems, energy management, etc. Some policies for countries are required to cope with urbanization problems. One of these policies is to build smart cities. Smart cities integrate information and communication technology and various physical devices connected to the network (the internet of things or IoT) to both improve the quality of government services and citizen welfare. This chapter presents a literature review of big data, smart cities, IoT, green-IoT concepts, using technology and methods, and applications worldwide.
The research of actual problems of development and implementation of digital transformation of municipal infrastructure development in the city through the creation of mechanisms to support the intellectualization of management in the industry is carried out. Intelligent monitoring of the housing and communal complex will improve the management system of territories and ensure timely adoption of adequate management decisions to coordinate the actions of economic entities that form the social and industrial infrastructure of municipalities. The most promising direction in this area is intelligent information systems.
The paper studies the current problems in the development and implementation of innovative strategies for the development of municipal infrastructure of the city through the creation of mechanisms to support the management intellectualization in the industry. Intelligent monitoring of housing and communal complex will allow improvement of the system of management of territories and ensuring the timely adoption of adequate administrative decisions on coordination of economic actors forming the social and production infrastructure of municipalities. The most promising direction in this field is geographic information systems.
In recent years, enormous amounts of digital data have been generated. In parallel, data collection, storage, and analysis technologies have developed. Recently, there has been an increasing trend of people moving towards urban areas. By 2030 more than 60% of the world’s population will live in an urban environment. Urban areas are big data resource because they include millions of citizens, technological devices, and vehicles which generate data continuously. Besides, rapid urbanization brings many challenges, such as environmental pollution, traffic congestion, health problems, energy management, etc. Some policies for countries are required to cope with urbanization problems. One of these policies is to build smart cities. Smart cities integrate information and communication technology and various physical devices connected to the network (the internet of things or IoT) to both improve the quality of government services and citizen welfare. This chapter presents a literature review of big data, smart cities, IoT, green-IoT concepts, using technology and methods, and applications worldwide.
