scholarly journals Optimal decision making model of battery energy storage-assisted electric vehicle charging station considering incentive demand response

2019 ◽  
Author(s):  
B. Upadhaya ◽  
Donghan Feng ◽  
Yun Zhou ◽  
Qiang Gui ◽  
Xiaojin Zhao ◽  
...  
Keyword(s):  
Decision Making ◽  
Energy Storage ◽  
Demand Response ◽  
Optimal Decision ◽  
Battery Energy Storage ◽  
Charging Station ◽  
Electric Vehicle Charging ◽  
Optimal Decision Making ◽  
Battery Energy ◽  
Decision Making Model

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 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 Adequacy Assessment of Wind Integrated Generating Systems Incorporating Demand Response and Battery Energy Storage System

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2649 ◽  
Author(s):  
Jiashen Teh
Keyword(s):  
Energy Storage ◽  
Demand Response ◽  
Renewable Energy Sources ◽  
Storage System ◽  
Peak Load ◽  
Energy Storage System ◽  
Battery Energy Storage System ◽  
Battery Energy Storage ◽  
Battery Energy ◽  
Generating System

The demand response and battery energy storage system (BESS) will play a key role in the future of low carbon networks, coupled with new developments of battery technology driven mainly by the integration of renewable energy sources. However, studies that investigate the impacts of BESS and its demand response on the adequacy of a power supply are lacking. Thus, a need exists to address this important gap. Hence, this paper investigates the adequacy of a generating system that is highly integrated with wind power in meeting load demand. In adequacy studies, the impacts of demand response and battery energy storage system are considered. The demand response program is applied using the peak clipping and valley filling techniques at various percentages of the peak load. Three practical strategies of the BESS operation model are described in this paper, and all their impacts on the adequacy of the generating system are evaluated. The reliability impacts of various wind penetration levels on the generating system are also explored. Finally, different charging and discharging rates and capacities of the BESS are considered when evaluating their impacts on the adequacy of the generating system.

An optimal decision making model for supporting week hospital management

2010 ◽  
Vol 14 (1) ◽  
pp. 74-88 ◽  
Author(s):  
Domenico Conforti ◽  
Francesca Guerriero ◽  
Rosita Guido ◽  
Marco Matucci Cerinic ◽  
Maria Letizia Conforti
Keyword(s):  
Decision Making ◽  
Hospital Management ◽  
Optimal Decision ◽  
Optimal Decision Making ◽  
Decision Making Model
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