TY - GEN
T1 - Stochastic Distribution System Market Clearing and Settlement via Sample Average Approximation
AU - Do Prado, Josue C.
AU - Vakilzadian, Hamid
AU - Qiao, Wei
AU - Moller, Dietmar P.F.
N1 - Funding Information:
This work was supported in part by the U.S. National Science Foundation under CAREER Award ECCS-0954938 and the Brazilian National Council for Scientific and Technological Development (CNPq).
Publisher Copyright:
© 2018 IEEE.
PY - 2019/1/2
Y1 - 2019/1/2
N2 - The widespread use of distributed energy resources (DERs) in the distribution grid is changing the electricity sector worldwide in many ways. The integration of distributed renewable energy (DRE), demand response, and smart grid technologies (SGTs) can make the power systems more sustainable, efficient, and reliable. However, new entities and market mechanisms are needed to efficiently integrate those resources in the distribution grid. This paper presents a day-ahead energy and reserve market clearing and settlement model for a distribution system operator (DSO) considering the uncertainties of DRE at different nodes of the distribution grid. The problem is formulated as a stochastic optimization model, whose objective is to minimize the expected system operation cost over a specified time horizon. The problem is then solved using a Monte Carlo simulation algorithm based on sample average approximation (SAA). A case study is performed to demonstrate the effectiveness of the proposed model.
AB - The widespread use of distributed energy resources (DERs) in the distribution grid is changing the electricity sector worldwide in many ways. The integration of distributed renewable energy (DRE), demand response, and smart grid technologies (SGTs) can make the power systems more sustainable, efficient, and reliable. However, new entities and market mechanisms are needed to efficiently integrate those resources in the distribution grid. This paper presents a day-ahead energy and reserve market clearing and settlement model for a distribution system operator (DSO) considering the uncertainties of DRE at different nodes of the distribution grid. The problem is formulated as a stochastic optimization model, whose objective is to minimize the expected system operation cost over a specified time horizon. The problem is then solved using a Monte Carlo simulation algorithm based on sample average approximation (SAA). A case study is performed to demonstrate the effectiveness of the proposed model.
KW - Distributed energy resource (DER)
KW - Market clearing
KW - Sample average approximation
UR - http://www.scopus.com/inward/record.url?scp=85061787621&partnerID=8YFLogxK
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U2 - 10.1109/NAPS.2018.8600576
DO - 10.1109/NAPS.2018.8600576
M3 - Conference contribution
AN - SCOPUS:85061787621
T3 - 2018 North American Power Symposium, NAPS 2018
BT - 2018 North American Power Symposium, NAPS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 North American Power Symposium, NAPS 2018
Y2 - 9 September 2018 through 11 September 2018
ER -