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On Optimal Planning and Operation of...

Wang, Weilun,

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On Optimal Planning and Operation of Smart Grid Edge Devices: Electric Vehicles and Distributed Energy Resources /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	On Optimal Planning and Operation of Smart Grid Edge Devices: Electric Vehicles and Distributed Energy Resources // Weilun Wang.
作者:	Wang, Weilun,
面頁冊數:	1 electronic resource (197 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-11, Section: B.
Contained By:	Dissertations Abstracts International86-11B.
標題:	Electrical engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31932218
ISBN:	9798315736790

On Optimal Planning and Operation of Smart Grid Edge Devices: Electric Vehicles and Distributed Energy Resources /
Wang, Weilun,

On Optimal Planning and Operation of Smart Grid Edge Devices: Electric Vehicles and Distributed Energy Resources /Weilun Wang. - 1 electronic resource (197 pages)

Source: Dissertations Abstracts International, Volume: 86-11, Section: B.

The power system has undergone a remarkable transformation, evolving from traditional centralized paradigms to dispersed and dynamic characteristics marked by technological advancements and environmental considerations. As the demand for sustainable solutions continues to grow, electric vehicles (EVs) and distributed energy resources (DERs) have emerged as one of the most critical boosters towards decentralized, cleaner, and more adaptive energy systems. Associated with the rapid development of EVs, EV charging facilities are increasingly crucial in supporting the EV charging needs and alleviating the range anxiety of EV users but are facing challenges in both planning and operation, especially considering the interaction with multiple entities and EV uncertainties. In terms of DERs, integrating the increasingly penetrated DERs into the efficient operation of power systems has become a challenge. To address this, DER aggregation (DERA) has been expected to be an effective solution, while a gap still exists in embedding them into the operation of the power system and mitigating their negative influences to the power system. To this end, the dissertation explores efficient schemes for EVs and DERAs:(i). Chapter 2 introduces a bilevel EV charging facility planning model integrating traffic and distribution network operation models. The statistical charging time is modeled to address macro traffic patterns and micro charging behaviors. The model also incorporates social factors and driver behaviors and is transformed into a tractable formulation for practical computation.(ii). Chapter 3 studies a semi-decentralized real-time charging scheme in which the central operator and individual chargers collaborate to achieve optimal EV charging schedules. The uncertainties of EVs are captured by both the central operator and individual chargers over different time granularities.(iii). Chapter 4 presents a chance-constrained unit commitment model to determine system optimal operation plans with multi-transmission node DERAs (M-DERAs) based on uncertain distribution factors (DFs). A novel bounded hetero-dimensional mixture model is also proposed to describe the complex distribution of DFs.(iv). Chapter 5 explores an optimal node aggregation approach to establish the optimal M-DERA formation scheme. Moreover, a multi-parametric optimization-based approach is proposed to calculate accessible capacity expansion regions to assist M-DERAs' complex expansion planning problems.

English

ISBN: 9798315736790Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Electric vehicles

On Optimal Planning and Operation of Smart Grid Edge Devices: Electric Vehicles and Distributed Energy Resources /
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520 $a The power system has undergone a remarkable transformation, evolving from traditional centralized paradigms to dispersed and dynamic characteristics marked by technological advancements and environmental considerations. As the demand for sustainable solutions continues to grow, electric vehicles (EVs) and distributed energy resources (DERs) have emerged as one of the most critical boosters towards decentralized, cleaner, and more adaptive energy systems. Associated with the rapid development of EVs, EV charging facilities are increasingly crucial in supporting the EV charging needs and alleviating the range anxiety of EV users but are facing challenges in both planning and operation, especially considering the interaction with multiple entities and EV uncertainties. In terms of DERs, integrating the increasingly penetrated DERs into the efficient operation of power systems has become a challenge. To address this, DER aggregation (DERA) has been expected to be an effective solution, while a gap still exists in embedding them into the operation of the power system and mitigating their negative influences to the power system. To this end, the dissertation explores efficient schemes for EVs and DERAs:(i). Chapter 2 introduces a bilevel EV charging facility planning model integrating traffic and distribution network operation models. The statistical charging time is modeled to address macro traffic patterns and micro charging behaviors. The model also incorporates social factors and driver behaviors and is transformed into a tractable formulation for practical computation.(ii). Chapter 3 studies a semi-decentralized real-time charging scheme in which the central operator and individual chargers collaborate to achieve optimal EV charging schedules. The uncertainties of EVs are captured by both the central operator and individual chargers over different time granularities.(iii). Chapter 4 presents a chance-constrained unit commitment model to determine system optimal operation plans with multi-transmission node DERAs (M-DERAs) based on uncertain distribution factors (DFs). A novel bounded hetero-dimensional mixture model is also proposed to describe the complex distribution of DFs.(iv). Chapter 5 explores an optimal node aggregation approach to establish the optimal M-DERA formation scheme. Moreover, a multi-parametric optimization-based approach is proposed to calculate accessible capacity expansion regions to assist M-DERAs' complex expansion planning problems.
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