Machine learning roles in advancing the power network stability due to deployments of renewable energies and electric vehicles

Author(s):  
Mohamad Nassereddine

AbstractRenewable energy sources are widely installed across countries. In recent years, the capacity of the installed renewable network supports large percentage of the required electrical loads. The relying on renewable energy sources to support the required electrical loads could have a catastrophic impact on the network stability under sudden change in weather conditions. Also, the recent deployment of fast charging stations for electric vehicles adds additional load burden on the electrical work. The fast charging stations require large amount of power for short period. This major increase in power load with the presence of renewable energy generation, increases the risk of power failure/outage due to overload scenarios. To mitigate the issue, the paper introduces the machine learning roles to ensure network stability and reliability always maintained. The paper contains valuable information on the data collection devises within the power network, how these data can be used to ensure system stability. The paper introduces the architect for the machine learning algorithm to monitor and manage the installed renewable energy sources and fast charging stations for optimum power grid network stability. Case study is included.

2020 ◽  
Vol 4 (6) ◽  
pp. 539-550
Author(s):  
A. D. Gorbunova ◽  
I. A. Anisimov

Application of renewable energy sources is a relevant area of energy supply for urban infrastructure. In 2019, the share of energy produced by such sources reached 11% (for solar energy) and 22% (for wind energy) of the total energy produced during the year. However, these systems require an improvement in their efficiency that can be achieved by introducing electric vehicles. They can accumulate, store and transfer surplus energy to the city’s power grid. A solution to this problem is a smart charging infrastructure. The existing studies in the field of charging infrastructure organization for electric vehicles consider only models locating charging stations in the city or the calculation of their required number. These calculations are based on socio-economic factors and images of a potential owner of an electric vehicle. Therefore, the aim of this study is to develop a methodology for determining the location of charging stations and their required number. The calculation will include the operating features of the existing charging infrastructure, which has not been done before. Thus, the purpose of this article is to research the operation of the existing charging infrastructure. This will provide an opportunity to develop approaches to the energy supply of charging infrastructure and city’s power grid from renewable energy sources. The article presents an analysis of data on the number of charging sessions during the year, month and day. This data enable us to construct curves of the charging session number and suggest ways to conduct the next stages of this study. Doi: 10.28991/esj-2020-01251 Full Text: PDF


Author(s):  
Jianhui Wong ◽  
Yun Seng Lim

Electrical grid is no longer featured in a conventional way nowadays. Today, the growing of new technologies, primarily the distributed renewable energy sources and electric vehicles, has been integrated with the distribution networks causing several technical issues. As a result, the penetration of the renewable energy sources can be limited by the utility companies. Smart grid has been emerged as one of the solutions to the technical issues, hence allowing the usage of renewable and improving the energy efficiency of the electrical grid. The challenge is to develop an intelligent management system to maintain the balance between the generation and demand. This task can be performed by using energy storage system. As part of the smart grid, the deployment of energy storage system plays a critical role in stabilizing the voltage and frequency of the networks with renewable energy sources and electric vehicles. This book chapter illustrates the revolution and the roles of energy storage for improving the network performance.


2020 ◽  
Vol 184 ◽  
pp. 01070
Author(s):  
Ayani Nandi ◽  
Vikram Kumar Kamboj

Daily load demand for industrial, residential and commercial sectors are changing day by day. Also, inclusion of e-mobility has totally effected the operations of realistic power sector. Hence, to meet this time varying load demand with minimum production cost is very challenging. The proposed research work focuses on the mathematical formulation of profit based unit commitment problem of realistic power system considering the impact of battery electric vehicles, hybrid electric vehicles and plug in electric vehicles and its solution using Intensify Harris Hawks Optimizer (IHHO). The coordination of plants with each other is named as Unit commitment of plants in which the most economical patterns of the generating station is taken so as to gain low production cost with higher reliability. But with the increase in industrialization has affected the environment badly so to maintain the balance between the generation and environment a new thinking of generating low cost power with high reliability by causing less harm to environment i.e. less emission of flue gases is adopted by considering renewable energy sources.


Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 317 ◽  
Author(s):  
Jagdesh Kumar ◽  
Chethan Parthasarathy ◽  
Mikko Västi ◽  
Hannu Laaksonen ◽  
Miadreza Shafie-Khah ◽  
...  

The stringent emission rules set by international maritime organisation and European Directives force ships and harbours to constrain their environmental pollution within certain targets and enable them to employ renewable energy sources. To this end, harbour grids are shifting towards renewable energy sources to cope with the growing demand for an onshore power supply and battery-charging stations for modern ships. However, it is necessary to accurately size and locate battery energy storage systems for any operational harbour grid to compensate the fluctuating power supply from renewable energy sources as well as meet the predicted maximum load demand without expanding the power capacities of transmission lines. In this paper, the equivalent circuit battery model of nickel–cobalt–manganese-oxide chemistry has been utilised for the sizing of a lithium-ion battery energy storage system, considering all the parameters affecting its performance. A battery cell model has been developed in the Matlab/Simulink platform, and subsequently an algorithm has been developed for the design of an appropriate size of lithium-ion battery energy storage systems. The developed algorithm has been applied by considering real data of a harbour grid in the Åland Islands, and the simulation results validate that the sizes and locations of battery energy storage systems are accurate enough for the harbour grid in the Åland Islands to meet the predicted maximum load demand of multiple new electric ferry charging stations for the years 2022 and 2030. Moreover, integrating battery energy storage systems with renewables helps to increase the reliability and defer capital cost investments of upgrading the ratings of transmission lines and other electrical equipment in the Åland Islands grid.


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