Economic Operation Scheduling of Microgrid Integrated with Battery Swapping Station

Revankar, Swapnil R.; Kalkhambkar, Vaiju; Gupta, Pranda Prasanta; Kumbhar, Ganesh B.

doi:10.1007/s13369-022-06624-9

Cited by 7 publications

(2 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a further study [11], the impact of substituting charging stations with BSSs on the power system is investigated within a 32-bus system. The ideal approach of a MG and BSS as two distinct stakeholders with competing objectives, was outlined in the study referenced as [12], The authors in [12] proposed a bi-level optimization framework aimed at maximizing the profit of the BSS at a lower level, while simultaneously minimizing the cost of the MG at a higher level. In another paper [13], researchers provided a two-tiered model in which a solution system was implemented to simultaneously minimize the cost of the BSS and maximize the performance of the island MG. For real-time energy management of a BSS-based MG, reference [14] suggested a Lyapunov optimization methodology based on queuing theory.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Placement of Battery Swapping Stations for Power Quality Improvement: A Novel Multi Techno-Economic Objective Function Approach

Al-Zaidi,

Inan

2023

Energies

View full text Add to dashboard Cite

In recent years, battery swapping stations have become increasingly popular in smart energy networks. Its advantages include reducing the time required for recharging energy, balancing the grid’s load, and extending the battery’s lifespan. Despite efforts focused on the placement and operation of battery swapping stations (BSSs), there is still a lack of a comprehensive and systematic examination that covers all aspects of both the economic and technical aspects of the power network. This encompasses considerations such as power quality and reliability, particularly in terms of where these stations should be located. This paper introduces a novel framework for strategically positioning BSS within smart microgrids that integrate distributed energy resources (DERs). It takes into account various technical factors (such as reliability and power quality) and economic factors (like the cost of generation and operation), which have been overlooked in the previous research. To achieve this goal, a unique hybrid optimization strategy is developed, incorporating a combination of epsilon-constraint and lexicographic (DECL) optimization methods. This approach tackles a multi-objective challenge, treating factors like the number, locations, and sizes of BSS as independent variables, while operational costs and technical power quality metrics are considered dependent variables. To validate this approach, it is tested on standard benchmark distribution power networks such as IEEE 33, 69, and 118 bus systems. The simulation results, showcasing the strengths and capabilities of this innovative strategy, are compared to the findings of previous research studies.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Optimal Placement of Battery Swapping Stations for Power Quality Improvement: A Novel Multi Techno-Economic Objective Function Approach

Al-Zaidi,

Inan

2023

Energies

View full text Add to dashboard Cite

show abstract

“…In recent years, electric vehicle (EV) technology has developed rapidly, driven by breakthroughs in battery technology [ 1 , 2 ]. As a substitute for fossil fuel vehicles, EVs have received extensive attention due to their environmentally friendly characteristics [ 2 , 3 , 4 , 5 ]. EVs can reduce traffic pollution emissions and have lower charging costs than refueling, which has been widely accepted and deployed [ 6 , 7 , 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning for Charging Scheduling of Electric Vehicles Considering Distribution Network Voltage Stability

Liu

Zeng

Cui

et al. 2023

Sensors

View full text Add to dashboard Cite

The rapid development of electric vehicle (EV) technology and the consequent charging demand have brought challenges to the stable operation of distribution networks (DNs). The problem of the collaborative optimization of the charging scheduling of EVs and voltage control of the DN is intractable because the uncertainties of both EVs and the DN need to be considered. In this paper, we propose a deep reinforcement learning (DRL) approach to coordinate EV charging scheduling and distribution network voltage control. The DRL-based strategy contains two layers, the upper layer aims to reduce the operating costs of power generation of distributed generators and power consumption of EVs, and the lower layer controls the Volt/Var devices to maintain the voltage stability of the distribution network. We model the coordinate EV charging scheduling and voltage control problem in the distribution network as a Markov decision process (MDP). The model considers uncertainties of charging process caused by the charging behavior of EV users, as well as the uncertainty of uncontrollable load, system dynamic electricity price and renewable energy generation. Since the model has a dynamic state space and mixed action outputs, a framework of deep deterministic policy gradient (DDPG) is adopted to train the two-layer agent and the policy network is designed to output discrete and continuous control actions. Simulation and numerical results on the IEEE-33 bus test system demonstrate the effectiveness of the proposed method in collaborative EV charging scheduling and distribution network voltage stabilization.

show abstract

Day-ahead dispatch of novel battery charging and swapping station based on distributionally robust optimization

Zhao

Yang

Qin

et al. 2023

Journal of Energy Storage

View full text Add to dashboard Cite

Economic Operation Scheduling of Microgrid Integrated with Battery Swapping Station

Cited by 7 publications

References 51 publications

Optimal Placement of Battery Swapping Stations for Power Quality Improvement: A Novel Multi Techno-Economic Objective Function Approach

Optimal Placement of Battery Swapping Stations for Power Quality Improvement: A Novel Multi Techno-Economic Objective Function Approach

Deep Reinforcement Learning for Charging Scheduling of Electric Vehicles Considering Distribution Network Voltage Stability

Day-ahead dispatch of novel battery charging and swapping station based on distributionally robust optimization

Contact Info

Product

Resources

About