scholarly journals Optimal Photovoltaic/Battery Energy Storage/Electric Vehicle Charging Station Design Based on Multi-Agent Particle Swarm Optimization Algorithm

2019 ◽  
Vol 11 (7) ◽  
pp. 1973 ◽  
Author(s):  
Qiongjie Dai ◽  
Jicheng Liu ◽  
Qiushuang Wei
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Storage ◽  
Electric Vehicle ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Battery Energy Storage ◽  
Charging Station ◽  
Electric Vehicle Charging ◽  
Multi Agent ◽  
Battery Energy

In order to effectively improve the utilization rate of solar energy resources and to develop sustainable urban efficiency, an integrated system of electric vehicle charging station (EVCS), small-scale photovoltaic (PV) system, and battery energy storage system (BESS) has been proposed and implemented in many cities around the world. This paper proposes an optimization model for grid-connected photovoltaic/battery energy storage/electric vehicle charging station (PBES) to size PV, BESS, and determine the charging/discharging pattern of BESS. The multi-agent particle swarm optimization (MAPSO) algorithm solves this model is solved, which combines multi-agent system (MAS) and the mechanism of particle swarm optimization (PSO). In this model, a load simulation model is presented to simulate EV charging patterns and to calculate the EV charging demand at each time interval. Finally, a case in Shanghai, China is conducted and three scenarios are analyzed to prove the effectiveness of the proposed model. A comparative analysis is also performed to show the superiority of MAPSO algorithm.

scholarly journals Optimal Planning of Electric Vehicle Charging Station Considering Mutual Benefit of Users and Power Grid

2021 ◽  
Vol 12 (4) ◽  
pp. 244
Author(s):  
Hui Hou ◽  
Junyi Tang ◽  
Bo Zhao ◽  
Leiqi Zhang ◽  
Yifan Wang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Electric Vehicle ◽  
Power Grid ◽  
Particle Swarm ◽  
Optimal Planning ◽  
Mutual Benefit ◽  
Swarm Optimization ◽  
Charging Station ◽  
Electric Vehicle Charging ◽  
Charging Stations

A reasonable plan for charging stations is critical to the widespread use of electric vehicles. In this paper, we propose an optimal planning method for electric vehicle charging stations. First of all, we put forward a forecasting method for the distribution of electric vehicle fast charging demand in urban areas. Next, a new mathematical model that considers the mutual benefit of electric vehicle users and the power grid is set up, aiming to minimize the social cost of charging stations. Then, the model is solved by the Voronoi diagram combined with improved particle swarm optimization. In the end, the proposed method is applied to an urban area, simulation results demonstrate that the proposed method can yield optimal location and capacity of each charging station. A contrasting case is carried out to verify that improved particle swarm optimization is more effective in finding the global optimal solution than particle swarm optimization.

scholarly journals Logistics Design for Mobile Battery Energy Storage Systems

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1157 ◽  
Author(s):  
Hassan Hayajneh ◽  
Xuewei Zhang
Keyword(s):  
Energy Storage ◽  
Electric Vehicle ◽  
Storage Systems ◽  
Design Parameters ◽  
Energy Storage Systems ◽  
Battery Energy Storage ◽  
Charging Station ◽  
Electric Vehicle Charging ◽  
Battery Energy Storage Systems ◽  
Battery Energy

Currently, there are three major barriers toward a greener energy landscape in the future: (a) Curtailed grid integration of energy from renewable sources like wind and solar; (b) The low investment attractiveness of large-scale battery energy storage systems; and, (c) Constraints from the existing electric infrastructure on the development of charging station networks to meet the increasing electrical transportation demands. A new conceptual design of mobile battery energy storage systems has been proposed in recent studies to reduce the curtailment of renewable energy while limiting the public costs of battery energy storage systems. This work designs a logistics system in which electric semi-trucks ship batteries between the battery energy storage system and electric vehicle charging stations, enabling the planning and operation of power grid independent electric vehicle charging station networks. This solution could be viable in many regions in the United States (e.g., Texas) where there are plenty of renewable resources and little congestion pressure on the road networks. With Corpus Christi, Texas and the neighboring Chapman Ranch wind farm as the test case, this work implement such a design and analyze its performance based on the simulation of its operational processes. Further, we formulate an optimization problem to find design parameters that minimize the total costs. The main design parameters include the number of trucks and batteries. The results in this work, although preliminary, will be instrumental for potential stakeholders to make investment or policy decisions.

scholarly journals Portfolio Optimization of Photovoltaic/Battery Energy Storage/Electric Vehicle Charging Stations with Sustainability Perspective Based on Cumulative Prospect Theory and MOPSO

2020 ◽  
Vol 12 (3) ◽  
pp. 985 ◽  
Author(s):  
Jicheng Liu ◽  
Qiongjie Dai
Keyword(s):  
Energy Storage ◽  
Portfolio Optimization ◽  
Prospect Theory ◽  
Electric Vehicle ◽  
Optimization Model ◽  
Cumulative Prospect Theory ◽  
Battery Energy Storage ◽  
Electric Vehicle Charging ◽  
Battery Energy ◽  
Charging Stations

Recently, an increasing number of photovoltaic/battery energy storage/electric vehicle charging stations (PBES) have been established in many cities around the world. This paper proposes a PBES portfolio optimization model with a sustainability perspective. First, various decision-making criteria are identified from perspectives of economy, society, and environment. Secondly, the performance of alternatives with respect to each criterion is evaluated in the form of trapezoidal intuitionistic fuzzy numbers (TrIFN). Thirdly, the alternatives are ranked based on cumulative prospect theory. Then, a multi-objective optimization model is built and solved by multi-objective particle swarm optimization (MOPSO) algorithm to determine the optimal PBES portfolio. Finally, a case in South China is studied and a scenario analysis is conducted to verify the effectiveness of the proposed model.

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