Optimal Futures Market Strategies for Energy Service Providers

Author(s):  
Petter L. Skantze ◽  
Marija D. Ilic
Author(s):  
Grygorii Monastyrskyi ◽  
Olena Borysiak

Introduction. Climate change, limitation of natural energy resources indicate the increasing role of ecological and energy security. The actual issue is the usage of ecological types of transport, improving the municipal transport management system. According to this, the article is devoted to research of innovative directions of development of municipal transport logistics based on sustainable development principles. Methods. The methodological basis of the research is the general scientific and economic-statistical methods: analysis, synthesis, statistical method – to investigate the international experience of reforming the transport system; inductions and deductions – to determine directions of development of municipal transport logistics of Ukraine; abstract-logical, economic-mathematical programming – to evaluate the conditions of the using ecological and energy efficient approaches to providing innovative development of the transport system in cities. Results. The article investigates the international experience of reforming the transport system on the basis of municipal ecology and development of «smart» cities. In the context of the using ecological and energy efficient approaches to reforming municipal transport logistics, public transport, the topical issue is the development of algorithms for implementing the Internet of things and artificial intelligence into the transport system. The priority directions of innovative development of municipal transport logistics in Ukraine are the digitization of processes of traffic management, diversification of ecological and energy efficient types of transport, forming partnerships between energy service companies and transport service providers. The article evaluates the conditions of the using ecological and energy efficient approaches to providing innovative development of the transport system in cities. This process is aimed at optimization of the usage of ecological types of transport, highlighting common priorities of the transport logistics development. The modeling of relationship between the usage of ecological types of transport and the level of emissions of harmful substances into the atmosphere is considered. For the purpose of innovative development of municipal transport logistics on the principles of municipal ecology and energy efficiency, the improvement of bicycle infrastructure in cities, the development of partnerships between energy service companies and public transport service providers are proposed. Discussion. The prospect of further research is to develop a model for optimizing the management of ecological and energy efficient transport in cities, developing partnerships between energy service companies and public transport service providers.


2017 ◽  
Vol 143 (5) ◽  
pp. 04017030 ◽  
Author(s):  
Jamshid Aghaei ◽  
Mansour Charwand ◽  
Mohsen Gitizadeh ◽  
Alireza Heidari

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 80 ◽  
Author(s):  
Yi Shen ◽  
Wei Fang ◽  
Feng Ye ◽  
Michel Kadoch

With the development of the Internet of Things (IoT) and the widespread use of electric vehicles (EV), vehicle-to-grid (V2G) has sparked considerable discussion as an energy-management technology. Due to the inherently high maneuverability of EVs, V2G systems must provide on-demand service for EVs. Therefore, in this work, we propose a hybrid computing architecture based on fog and cloud with applications in 5G-based V2G networks. This architecture allows the bi-directional flow of power and information between schedulable EVs and smart grids (SGs) to improve the quality of service and cost-effectiveness of energy service providers. However, it is very important to select an EV suitable for scheduling. In order to improve the efficiency of scheduling, we first need to determine define categories of target EV users. We found that grouping on the basis of EV charging behavior is one effective method to identify target EVs. Therefore, we propose a hybrid artificial intelligence classification method based on the charging behavior profile of EVs. Through this classification method, target EVs can be accurately identified. The results of cross-validation experiments and performance evaluations suggest that this method is effective.


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