Abstract
This chapter focuses on the top level of the proposed architecture: a set of neighborhoods connected to different buses forming into a microgrid. The microgrid can span over the entire distribution system downstream of a distribution substation. In this aggregation level, a microgrid energy manager is in charge of keeping the power balances over the distribution network. Specifically, we consider risk-constrained optimal microgrid energy management and minimize microgrid operating cost while maintaining power quality and system reliability. A risk-constrained two-stage stochastic program is formulated to address uncertainties in electricity prices and renewable power output for the microgrid, and an efficient solution algorithm is developed to solve the formulated problem. Extensive simulation results show that by using our energy management approach, the microgrid can effectively reduce the operating cost while constraining the risk and ensuring the system reliability.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Efficiency Benefits.
Miguel C. and J. Arroyo M. A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem. Power Systems, IEEE Transactions on, 21(3): 1371–1378, August 2006.
Mohammad E. Khodayar, Masoud Barati, and Mohammad Shahidehpour. Integration of high reliability distribution system in microgrid operation. Smart Grid, IEEE Transactions on, 3(4):1997–2006, December 2012.
Mohammad Tasdighi, Hassan Ghasemi, and Ashkan Rahimi-Kian. Residential microgrid scheduling based on smart meters data and temperature dependent thermal load modeling. Smart Grid, IEEE Transactions on, 5(1):349–357, 2014.
Masood Parvania, Mahmud Fotuhi-Firuzabad, and Mohammad Shahidehpour. Optimal demand response aggregation in wholesale electricity markets. Smart Grid, IEEE Transactions on, 4(4):1957–1965, December 2013.
Ali T Al-Awami and Eric Sortomme. Coordinating vehicle-to-grid services with energy trading. Smart Grid, IEEE Transactions on, 3(1):453–462, 2012.
Jianhui Wang, Mohammad Shahidehpour, and Zuyi Li. Security-constrained unit commitment with volatile wind power generation. Power Systems, IEEE Transactions on, 23(3):1319–1327, August 2008.
R Tyrrell Rockafellar and Stanislav Uryasev. Optimization of conditional value-at-risk. Journal of risk, 2:21–42, 2000.
Jitka Dupačová, Nicole Gröwe-Kuska, and Werner Römisch. Scenario reduction in stochastic programming. Mathematical programming, 95(3):493–511, 2003.
Ehsan Hajipour, Mokhtar Bozorg, and Mahmud Fotuhi-Firuzabad. Stochastic capacity expansion planning of remote microgrids with wind farms and energy storage. Sustainable Energy, IEEE Transactions on, 6(2):491–498, April 2015.
GUROBI, 2015 (accessed February 3, 2015).
Distribution Test Feeders.
National Wind Technology Center M2 Tower, 2015 (accessed February 3, 2015).
PJM - Markets and Operations, 2015 (accessed February 3, 2015).
Hung D Nguyen and Konstantin Turitsyn. Voltage multi-stability in distribution grids with power flow reversal. arXiv preprint arXiv:1404.6581, 2014.
SX Chen, Hoay Beng Gooi, and MingQiang Wang. Sizing of energy storage for microgrids. Smart Grid, IEEE Transactions on, 3(1):142–151, March 2012.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2017 The Author(s)
About this chapter
Cite this chapter
Guo, Y., Fang, Y., Khargonekar, P.P. (2017). Risk-Constrained Optimal Energy Management for Smart Microgrids. In: Stochastic Optimization for Distributed Energy Resources in Smart Grids. SpringerBriefs in Electrical and Computer Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-59529-0_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-59529-0_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59528-3
Online ISBN: 978-3-319-59529-0
eBook Packages: Computer ScienceComputer Science (R0)