scholarly journals Strategy of Large-Scale Electric Vehicles Absorbing Renewable Energy Abandoned Electricity Based on Master-Slave Game

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Dunnan Liu ◽  
Lingxiang Wang ◽  
Weiye Wang ◽  
Hua Li ◽  
Mingguang Liu ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Electric Vehicles ◽  
Large Scale

scholarly journals Hierarchical Multiobjective Dispatching Strategy for the Microgrid System Using Modified MOEA/D

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Xiaofeng Wan ◽  
Hai Lian ◽  
Xiaohua Ding ◽  
Jin Peng ◽  
Yining Wu ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Electric Vehicles ◽  
Large Scale ◽  
Load Level ◽  
Energy Output ◽  
Power Fluctuation ◽  
Electricity Cost ◽  
Distributed Solutions ◽  
User Demand ◽  
Grid Load

The large-scale electric vehicles connected to the microgrid have brought various challenges to the safe and economic operation of the microgrid. In this paper, a hierarchical microgrid dispatching strategy considering the user-side demand is proposed. According to the operation characteristics of each dispatch unit, the strategy divides the microgrid system into two levels: source-load level and source-grid-load level. At the source-load level, priority should be given to the use of the renewable energy output. On the basis of considering the user demand, energy storage, electric vehicles, and dispatchable loads should be utilized to maximize the consumption of the renewable energy and minimize the user’s electricity cost. The source-grid-load level can smooth the tie-line power fluctuation through dispatching of the power grid and diesel generators. Furthermore, the study presents a modified MOEA/D algorithm to solve the hierarchical scheduling problem. In the proposed algorithm, a modified Tchebycheff decomposition method is introduced to obtain uniformly distributed solutions. In addition, initialization and replacement strategies are introduced to enhance the convergence and diversity. A wind-photovoltaic-diesel-storage hybrid power system is considered to verify the performance of the proposed dispatching strategy and the modified algorithm. The obtained results are compared with other dispatching approaches, and the comparisons confirm the effectiveness and scientificity of the proposed strategy and algorithm.

scholarly journals The Synergies of Shared Autonomous Electric Vehicles with Renewable Energy in a Virtual Power Plant and Microgrid

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2016 ◽  
Author(s):  
Riccardo Iacobucci ◽  
Benjamin McLellan ◽  
Tetsuo Tezuka
Keyword(s):  
Renewable Energy ◽  
Power Plant ◽  
Electric Vehicles ◽  
Large Scale ◽  
Renewable Energy Sources ◽  
Transport Sector ◽  
Virtual Power ◽  
Virtual Power Plant ◽  
Vehicle Charging ◽  
Autonomous Electric Vehicles

The introduction of shared autonomous electric vehicles (SAEVs), expected within the next decade, can transform the car into a service, accelerate electrification of the transport sector, and allow for large scale control of electric vehicle charging. In this work, we investigate the potential for this system to provide aggregated storage when combined with intermittent renewable energy sources. We develop a simulation methodology for the optimization of vehicle charging in the context of a virtual power plant or microgrid, with and without grid connection or distributed dispatchable generators. The model considers aggregate storage availability from vehicles based on transport patterns taking into account the necessary vehicle redistribution. We investigate the case of a grid-connected VPP with rooftop solar and the case of a isolated microgrid with solar, wind, and dispatchable generation. We conduct a comprehensive sensitivity analysis to study the effect of several parameters on the results for both cases.

Energy After COVID

2020 ◽  
Vol 119 (820) ◽  
pp. 317-322
Author(s):  
Michael T. Klare
Keyword(s):  
Renewable Energy ◽  
Electric Vehicles ◽  
Fossil Fuels ◽  
Fossil Fuel ◽  
White Collar ◽  
Peak Oil ◽  
Business Travel ◽  
Reduce Energy Consumption ◽  
The Cost ◽  
Energy Companies

By transforming patterns of travel and work around the world, the COVID-19 pandemic is accelerating the transition to renewable energy and the decline of fossil fuels. Lockdowns brought car commuting and plane travel to a near halt, and the mass experiment in which white-collar employees have been working from home may permanently reduce energy consumption for business travel. Renewable energy and electric vehicles were already gaining market share before the pandemic. Under pressure from investors, major energy companies have started writing off fossil fuel reserves as stranded assets that are no longer worth the cost of extracting. These shifts may indicate that “peak oil demand” has arrived earlier than expected.

Assessment of bioenergy development potential in agricultural enterprises

2020 ◽  
pp. 165-171
Author(s):  
Iryna Hryhoruk
Keyword(s):  
Renewable Energy ◽  
Large Scale ◽  
Agricultural Waste ◽  
General Structure ◽  
Geographical Location ◽  
Economic Assessment ◽  
Energy Resources ◽  
Industrial Complex ◽  
Renewable Energy Resources ◽  
Energy Potential

Exhaustion of traditional energy resources, their uneven geographical location, and catastrophic changes in the environment necessitate the transition to renewable energy resources. Moreover, Ukraine's economy is critically dependent on energy exports, and in some cases, the dependence is not only economic but also political, which in itself poses a threat to national security. One of the ways to solve this problem is the large-scale introduction and use of renewable energy resources, bioenergy in particular. The article summarizes and offers methods for assessing the energy potential of agriculture. In our country, a significant amount of biomass is produced every year, which remains unused. A significant part is disposed of due to incineration, which significantly harms the environment and does not allow earning additional funds. It is investigated that the bioenergy potential of agriculture depends on the geographical distribution and varies in each region of Ukraine. Studies have shown that as of 2019 the smallest share in the total amount of conventional fuel that can be obtained from agricultural waste and products suitable for energy production accounts for Zakarpattya region - 172.5 thousand tons. (0.5% of the total) and Chernivtsi region - 291.3 thousand tons. (0.9%). Poltava region has the greatest potential - 2652.2 thousand tons. (7.8%) and Vinnytsia - 2623.7 thousand tons. (7.7%). It should be noted that the use of the energy potential of biomass in Ukraine can be called unsatisfactory. The share of biomass in the provision of primary energy consumption is very small. For bioenergy to occupy its niche in the general structure of the agro-industrial complex, it is necessary to develop mechanisms for its stimulation. In addition, an effective strategy for the development of the bioenergy sector of agriculture is needed. The article considers the general energy potential of agriculture, its indicative structure. The analysis is also made in terms of areas. In addition, an economic assessment of the possible use of existing potential is identified.

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

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Mohamad Nassereddine
Keyword(s):  
Machine Learning ◽  
Renewable Energy ◽  
Electric Vehicles ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Power Network ◽  
Network Stability ◽  
Fast Charging ◽  
Electrical Loads ◽  
Charging Stations

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.

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