Optimal behavior of electric vehicle parking lots as demand response aggregation agents

Shafie‐khah, Miadreza; Heydarian‐Forushani, Ehsan; Osório, Gerardo J.; Gil, Fabio A. S.; Aghaei, Jamshid; Barani, Mostafa

doi:10.1109/pesgm.2016.7741167

Cited by 30 publications

(43 citation statements)

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“…In [27], the participation level of EV parking station in different price-based and incentive-based DR programs was optimized using a stochastic programming to maximize the operator's profit. Similar method was used in [28] to design a DR offering/bidding strategy that allows the parking station to take part in the intraday Demand Response eXchange (DRX) market [29] as an aggregation agent.…”

Section: Related Workmentioning

confidence: 99%

“…1) The works in [7][8][9][10], [18] focused on the EV charging coordination, while those in [27], [28], [30] optimized the amount of energy traded in different energy markets. The proposed work explores the role of EV parking station as both DR and charging service providers, aiming to optimize the individual charging schedule of each EV while being able to fulfill the demand curtailment request from utility.…”

Section: Related Workmentioning

confidence: 99%

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A Real-Time Charging Scheme for Demand Response in Electric Vehicle Parking Station

Yao

Lim

Tsai

2017

IEEE Trans. Smart Grid

245

104

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Real-Time Charging Scheme for Demand Response in Electric Vehicle Parking Station

Yao

Lim

Tsai

2017

IEEE Trans. Smart Grid

245

104

View full text Add to dashboard Cite

show abstract

“…DR can be incentive‐based (LC), price‐based (an effective pricing policy), and demand reduction bids based (demand bidding/buyback programs) to the consumer to connect/disconnect their load depending on the signal. Shafie‐Khah et al compared DR program for a price‐based and incentive‐based with different subprograms such as ToU, real‐time pricing (RTP), critical peak pricing (CPP), and emergency demand response program (EDRP). Controlling method for the load is also proposed, such as Interruptible/Curtailable (I/C services), DLC.…”

Section: Factors To Be Considered For Ems Implementationmentioning

confidence: 99%

Energy management system for smart grid: An overview and key issues

2020

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Summary Energy crisis and the global impetus to “go green” have encouraged the integration of renewable energy resources, plug‐in electric vehicles, and energy storage systems to the grid. The presence of more than one energy source in the grid necessitates the need for an efficient energy management system to guide the flow of energy. Moreover, the variability and volatile nature of renewable energy sources, uncertainties associated with plug‐in electric vehicles, the electricity price, and the time‐varying load bring new challenges to the power engineers to achieve demand‐supply balance for stable operation of the power system. The energy management system can effectively coordinate the energy sharing/trading among all available energy resources, and supply loads economically in all the conditions for the reliable, secure, and efficient operation of the power system. This paper reviews the framework, objectives, architecture, benefits, and challenges of the energy management system with a comprehensive analysis of different stakeholders and participants involved in it. The review paper gives a critical analysis of the distributed energy resources behavior and different programs such as demand response, demand‐side management, and power quality management implemented in the energy management system. Different uncertainty quantification methods are also summarized. This review paper also presents a comparative and critical analysis of the main optimization techniques used to achieve different energy management system objectives while satisfying multiple constraints. Thus, the review offers numerous recommendations for research and development of the cutting‐edge optimized energy management system applicable for homes, buildings, industries, electric vehicles, and the whole community.

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“…Moreover, the role of the EVs in DR is not discernible. Optimal strategies of parking lots is derived as responsive demands, in both price‐based (RTP DR, TOU DR, Emergency DR, and CPP DR) and incentive‐based DR programs (interruptible/curtailabe DR). The impacts of different DR programs on the operational behavior of parking lots in an electricity market are proposed, which optimizes the participation level of parking lots.…”

Section: Electric Vehicles and Flexible Loads In Drmentioning

confidence: 99%

“…Optimal strategies of parking lots is derived as responsive demands, in both price‐based (RTP DR, TOU DR, Emergency DR, and CPP DR) and incentive‐based DR programs (interruptible/curtailabe DR). The impacts of different DR programs on the operational behavior of parking lots in an electricity market are proposed, which optimizes the participation level of parking lots. A distributed multiagent EV management system is presented, which satisfies the energy requirements of a large number of EVs based on Nash certainty equivalence principle.…”

Section: Electric Vehicles and Flexible Loads In Drmentioning

confidence: 99%

Opportunities and challenges of demand response in active distribution networks

Ponnaganti

Pillai

Bak‐Jensen

2017

WIREs Energy & Environment

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In power systems, the installed generation capacity must exceed the annual peak demand, even though some capacity is kept idle most of the time. However, if it is uneconomical or not feasible to augment a sufficient capacity, the demand might exceed the available capacity. This mandates the system operator to shed the load in order to maintain security of the system. With the advent of advanced smart metering infrastructure, communication between system operator and end-use customers makes it possible to adjust/curtail/shift the demand with respect to the state of the system. The response of the demand commonly termed as demand response (DR) can be attained either by incentive-based or pricebased. With the help of DR, the renewable energy generation capacity can be increased by tuning the demand to match the variable and unpredictable power from renewable generation. It can also bring other benefits such as peak shaving, hosting capacity enhancement, and generation cost reduction. Furthermore, electric vehicles, heat pumps, and electric water heater can also be used as distributed storage resources to contribute to ancillary services, such as frequency/ voltage regulation, peak-shaving power or help to integrate fluctuating renewable resources. All these DR modes of operation need conventional regulatory frameworks and market design for capitalizing the available resources. Therefore, the objective of the study is to discuss the DR classification and their control strategies, DR role in microgrids and integration of renewable energy resources. Also, highlighted the opportunities and challenges along with the insights for the research scope associated with DR.

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Optimal behavior of electric vehicle parking lots as demand response aggregation agents

Cited by 30 publications

References 0 publications

A Real-Time Charging Scheme for Demand Response in Electric Vehicle Parking Station

A Real-Time Charging Scheme for Demand Response in Electric Vehicle Parking Station

Energy management system for smart grid: An overview and key issues

Opportunities and challenges of demand response in active distribution networks

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