Optimal electric vehicles charging scheduling for energy and reserve markets considering wind uncertainty and generator contingency

Summary Decarburization of electrical systems encourage high wind power into electric power systems and the electrification of transport sectors through electric vehicles (EVs). The increasing penetration of uncertain wind power generation and transportation networks via EV charging stations has int...

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Published in:	International journal of energy research Vol. 46; no. 4; pp. 4516 - 4539
Main Authors:	Gupta, Pranda Prasanta, Kalkhambkar, Vaiju, Sharma, Kailash Chand, Jain, Prerna, Bhakar, Rohit
Format:	Journal Article
Language:	English
Published:	Chichester, UK John Wiley & Sons, Inc 25.03.2022
Subjects:	bender's decomposition Contingency Contingency ranking Decarburization Electric power Electric power generation Electric power sources Electric power systems electric vehicle Electric vehicle charging Electric vehicles Electricity Electricity distribution Energy Energy reserves generator response Operating costs Optimization Outages Ranking Scheduling Security stochastic security‐constrained unit commitment transportation network Transportation networks Uncertainty Unit commitment Vehicles Wind power Wind power generation wind uncertainty
ISSN:	0363-907X, 1099-114X
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Abstract	Summary Decarburization of electrical systems encourage high wind power into electric power systems and the electrification of transport sectors through electric vehicles (EVs). The increasing penetration of uncertain wind power generation and transportation networks via EV charging stations has introduced challenges for system operators to manage power systems and market operations. In this context, this paper presents a stochastic AC security‐constrained unit commitment (SCUC) model to clear day‐ahead energy and reserve markets considering transportation–electricity networks through EV charging stations in the presence of uncertain wind power and generator contingency. Pre‐contingency ranking is a common strategy for reducing the time of the SCUC problem, but it provides high‐impact outages. To address this issue, generator outages ranked first are identified using the post‐contingency generator response ranking approach. The main contribution of this paper is the development of a structure with the most effective outages in the presence of uncertain wind power and transportation networks. The stochastic optimization approach is modeled through scenarios with corresponding probabilities to manage the wind uncertainty. The computationally complex proposed model is solved by a prominent bender decomposition approach. Case studies are shown on a modified IEEE 118‐bus system with 26 nodes and 74 connected links of the electricity transportation network. The results show that optimal EV travel scheduling can optimize the transportation network and a sufficient energy reserve, confirming the security of the power system with minimum operating cost.
AbstractList	Decarburization of electrical systems encourage high wind power into electric power systems and the electrification of transport sectors through electric vehicles (EVs). The increasing penetration of uncertain wind power generation and transportation networks via EV charging stations has introduced challenges for system operators to manage power systems and market operations. In this context, this paper presents a stochastic AC security‐constrained unit commitment (SCUC) model to clear day‐ahead energy and reserve markets considering transportation–electricity networks through EV charging stations in the presence of uncertain wind power and generator contingency. Pre‐contingency ranking is a common strategy for reducing the time of the SCUC problem, but it provides high‐impact outages. To address this issue, generator outages ranked first are identified using the post‐contingency generator response ranking approach. The main contribution of this paper is the development of a structure with the most effective outages in the presence of uncertain wind power and transportation networks. The stochastic optimization approach is modeled through scenarios with corresponding probabilities to manage the wind uncertainty. The computationally complex proposed model is solved by a prominent bender decomposition approach. Case studies are shown on a modified IEEE 118‐bus system with 26 nodes and 74 connected links of the electricity transportation network. The results show that optimal EV travel scheduling can optimize the transportation network and a sufficient energy reserve, confirming the security of the power system with minimum operating cost. Summary Decarburization of electrical systems encourage high wind power into electric power systems and the electrification of transport sectors through electric vehicles (EVs). The increasing penetration of uncertain wind power generation and transportation networks via EV charging stations has introduced challenges for system operators to manage power systems and market operations. In this context, this paper presents a stochastic AC security‐constrained unit commitment (SCUC) model to clear day‐ahead energy and reserve markets considering transportation–electricity networks through EV charging stations in the presence of uncertain wind power and generator contingency. Pre‐contingency ranking is a common strategy for reducing the time of the SCUC problem, but it provides high‐impact outages. To address this issue, generator outages ranked first are identified using the post‐contingency generator response ranking approach. The main contribution of this paper is the development of a structure with the most effective outages in the presence of uncertain wind power and transportation networks. The stochastic optimization approach is modeled through scenarios with corresponding probabilities to manage the wind uncertainty. The computationally complex proposed model is solved by a prominent bender decomposition approach. Case studies are shown on a modified IEEE 118‐bus system with 26 nodes and 74 connected links of the electricity transportation network. The results show that optimal EV travel scheduling can optimize the transportation network and a sufficient energy reserve, confirming the security of the power system with minimum operating cost.
Author	Sharma, Kailash Chand Kalkhambkar, Vaiju Bhakar, Rohit Gupta, Pranda Prasanta Jain, Prerna
Author_xml	– sequence: 1 givenname: Pranda Prasanta surname: Gupta fullname: Gupta, Pranda Prasanta email: kvaijnath@gmail.com organization: Rajarambapu Institute of Technology – sequence: 2 givenname: Vaiju orcidid: 0000-0002-6449-8464 surname: Kalkhambkar fullname: Kalkhambkar, Vaiju organization: Rajarambapu Institute of Technology – sequence: 3 givenname: Kailash Chand surname: Sharma fullname: Sharma, Kailash Chand organization: Dr B R Ambedkar National Institute of Technology – sequence: 4 givenname: Prerna surname: Jain fullname: Jain, Prerna organization: Malaviya National Institute of Technology – sequence: 5 givenname: Rohit orcidid: 0000-0003-0155-9327 surname: Bhakar fullname: Bhakar, Rohit organization: Malaviya National Institute of Technology
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Snippet	Summary Decarburization of electrical systems encourage high wind power into electric power systems and the electrification of transport sectors through... Decarburization of electrical systems encourage high wind power into electric power systems and the electrification of transport sectors through electric...
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SubjectTerms	bender's decomposition Contingency Contingency ranking Decarburization Electric power Electric power generation Electric power sources Electric power systems electric vehicle Electric vehicle charging Electric vehicles Electricity Electricity distribution Energy Energy reserves generator response Operating costs Optimization Outages Ranking Scheduling Security stochastic security‐constrained unit commitment transportation network Transportation networks Uncertainty Unit commitment Vehicles Wind power Wind power generation wind uncertainty
Title	Optimal electric vehicles charging scheduling for energy and reserve markets considering wind uncertainty and generator contingency
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