ADMM-Based Coordination of Electric Vehicles in Constrained Distribution Networks Considering Fast Charging and Degradation

Zhou, Xu; Zou, Suli; Wang, Peng; Ma, Zhongjing

doi:10.1109/tits.2020.3015122

Cited by 38 publications

(14 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some optimization techniques have been applied for EV coordinated charging problems. The EV fast charging problem was modeled as an optimization coordination problem subject to the coupled feeder capacity constraints in the distribution network solved by a decentralized iterative algorithm based on the gradient projection methods [11]. A mixed-integer programming-based optimization model was used to minimize the cost of EV owners in [14] for EV coordination and V2G resource optimization.…”

Section: A Applications Of Optimization Methods In Coordinated Chargi...mentioning

confidence: 99%

“…A multi-objective collaborative planning strategy was proposed in [21] to minimize the overall annual cost of investment and energy losses and maximize the annual traffic flow to provide better service quality by reducing the waiting time at the station. Considering the need for fast charging and reducing degradation in batteries and the distribution network, a hierarchical model was proposed to achieve an optimal strategy profile for EVs and verified on a 5-feeder and a 12-feeder test systems [11]. The charging schedule for each charging pile over each hour during one day was investigated subjected to two conflicting objective functions, i.e., fluctuation of the power grid and the revenue, where WA-MOPSO was proposed to address the formulated MOO problem [15].…”

Section: B Optimization In Public Charging Stationsmentioning

confidence: 99%

See 1 more Smart Citation

Multi-objective Scheduling of Electric Vehicle Charging/Discharging with Time of Use Tariff

Song¹,

Liu²,

Jalili³

et al. 2021

Preprint

View full text Add to dashboard Cite

The increased uptake of electric vehicles (EVs) leads to increased demand for electricity, and sometime pressure to power grids. Uncoordinated charging of EVs may result in putting pressure on distribution networks, and often some form of optimisation is required in the charging process. Optimal coordinated charging is a multi-objective optimisation problem in nature, with objective functions such as minimum price charging and minimum disruptions to the grid. In this manuscript, we propose a general multi-objective EV charging/discharging schedule (MOEVCS) framework, where the time of use (TOU) tariff is designed according to the load request at each time stamp. To obtain the optimal scheduling scheme and balance the competing benefits from different stakeholders, such as EV owners, EV charging stations (EVCS), and the grid operator, we design three conflicting objective functions including EV owner cost, EVCS profit, and the network impact. Moreover, we create four application scenarios with different charging request distributions over the investigated periods. We use a constraint multi-objective evolutionary algorithm (MOEA) to solve the problem. Our results demonstrate the effectiveness of MOEVCS in making a balance between three conflicting objectives.

show abstract

Section: A Applications Of Optimization Methods In Coordinated Chargi...mentioning

confidence: 99%

Section: B Optimization In Public Charging Stationsmentioning

confidence: 99%

Multi-objective Scheduling of Electric Vehicle Charging/Discharging with Time of Use Tariff

Song¹,

Liu²,

Jalili³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Rivera et al [9] used the ADMM algorithm to schedule a fleet of electric vehicles under different DR aggregator system-level goals, such as cost optimization or valley filling. Zhou et al [10] extend the approach by also considering fast charging and electric vehicle battery degradation. In both works, the ADMM algorithm solves an optimal power exchange problem that is similar to our work, but only considers operational flexibility deployment from electric vehicle charging.…”

Section: A State Of the Art And Advancementsmentioning

confidence: 99%

Computational Performance Study on the Alternating Direction Method of Multipliers Algorithm for a Demand Response Peak Shaving Application

Schwarz

Monti

2023

IEEE Systems Journal

View full text Add to dashboard Cite

This article quantifies and benchmarks the computational performance of the distributed alternating direction method of multipliers (ADMM) optimization algorithm applied to a slowdynamics demand response peak shaving application. We propose a hierarchical demand response architecture, in which a commercial aggregator acts as the supplier and system-level flexibility service provider to a portfolio of residential prosumers. The prosumers are equipped with flexible distributed energy resources that we model based on convex operation constraints. The optimal day-ahead peak shaving control of the portfolio is realized in a distributed way through an entirely parallel implementation of the distributed ADMM optimization algorithm. In this scenario, we evaluate the ADMM algorithm's overall speed of convergence and conduct multiple large-scale numerical simulation experiments on a compute cluster to compare and assess the algorithmic performance of ADMM against a centralized reference benchmark optimization. Our simulation results show that the distributed ADMM algorithm is superior compared to a purely centralized optimization. The findings are of particular use to commercial demand response aggregators that show interest in the deployment and improvement of distributed coordination and optimal control concepts for future slow-dynamics demand response applications and services.

show abstract

“…The increasing growth of EVs has also faced power systems with some challenges. Some of the major challenges are the emergence of a new peak in load profile [6], increase in power grid losses [7], damage to power system equipment such as transformers and cables [8], and uncertainties in EV users' behavior (e.g., the arrival and departure time of EVs) [9].…”

Section: Nomenclaturementioning

confidence: 99%

“…Because of the non-linear objective function and constraints, a mixed-integer non-linear programming (MINLP) solver is employed. For this purpose, the basic open-source non-linear mixed-integer programming (BONMIN) optimization framework is used to optimize (7) due to the superior performance compared with metaheuristic optimization methods. The BON-MIN solver combines the interior-point optimization approach and the coin-or branch and cut method to solve MINLP optimization problems [34].…”

Section: Reconfiguration Constraintsmentioning

confidence: 99%

Overarching Sustainable Energy Management of PV Integrated EV Parking Lots in Reconfigurable Microgrids Using Generative Adversarial Networks

Madahi

Kamrani

Nafisi

2022

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

In recent years, environmental issues have motivated the wide usage of electric vehicles (EVs) due to their zero tailpipe emission. However, this trend can pose severe challenges to power systems, such as decreasing equipment lifetime. Moreover, the CO 2 emission of EVs is closer to that of internal combustion engine vehicles in some cases due to the carbon footprint of EV charging. Modeling the uncertain nature of EV users' behavior is another obstacle due to the complex dynamics of these uncertainties. To overcome these problems, an overarching day-ahead smart charging method is proposed in this paper from the perspective of distribution system operators (DSOs), EV users, and governments simultaneously. The aim of the proposed method is to minimize the operating cost of microgrids, the degradation cost of EV batteries, and emission cost by scheduling the active and reactive power of EV parking lots integrated with photovoltaic (PV) systems as well as finding the optimum network configuration. Previous model-based methods cannot appropriately model uncertainties in EV users' behavior because of some statistical assumptions. Nevertheless, this paper employs data-driven methods based on generative adversarial networks (GAN) to represent these uncertainties. The performance of the proposed method is evaluated by implementing it on a real reconfigurable microgrid. The results show that using the proposed method, the DSO and emission costs can reduce by 11.96% and 3.37% compared to the uncoordinated charging of EVs, respectively. Furthermore, the share of sustainable energy in EV charging increases by 9% using the proposed method.

show abstract

ADMM-Based Coordination of Electric Vehicles in Constrained Distribution Networks Considering Fast Charging and Degradation

Cited by 38 publications

References 44 publications

Multi-objective Scheduling of Electric Vehicle Charging/Discharging with Time of Use Tariff

Multi-objective Scheduling of Electric Vehicle Charging/Discharging with Time of Use Tariff

Computational Performance Study on the Alternating Direction Method of Multipliers Algorithm for a Demand Response Peak Shaving Application

Overarching Sustainable Energy Management of PV Integrated EV Parking Lots in Reconfigurable Microgrids Using Generative Adversarial Networks

Contact Info

Product

Resources

About