Optimal reconfiguration and scheduling of a smart distribution network with uncertain renewables and price-responsive demand
Abstract
A new two-stage operation scheduling framework is proposed in this paper to optimize day-ahead (DA) operation of a reconfigurablesmart distribution network (SDN). The SDN contains wind farm as uncertain renewable generation as well asresponsive demand and is operated by a distribution company (DisCo). The DisCo implements nodal hourly pricing as a pricebaseddemand response program (DRP) to modify consumers’ demand profile. Retail prices are determined in the first stageof the proposed scheduling framework, while the best network topology and the bidding strategy of the DisCo in the DA energymarket are determined in the second stage. The two point estimate method (TPEM) is implemented in this paper to modelthe intrinsic uncertainty of wind farm power generation and responsive demand. Finally, the effectiveness of the proposedframework is evaluated in several case studies.References
[1] Gams webpage. URL http://www.gams.com.
[2] Ontario energy board (oeb). regulated price plan manual.
URL http://www.ontarioenergyboard.ca/OEB/Document
s/EB-2004-0205/RPP-Manual pdf.
[3] http://www.ee.washington.edu/research/pstca/pf30/pg_tca30bus.html.
[4] H. A. Aalami, M. ParsaMoghaddam, and G. R. Yousefi. Demand response
modeling considering interruptible/curtailable loads and capacity
market programs. Applied Ener, 87:243–250, 2010.
[5] G. Angevine and D. Hrytzak-Lieffers. Ontario industrial electricity demand
responsiveness to price. The Fraser Institute, 2007.
[6] M. Doostizadeh and H. Ghasemi. A day-ahead electricity pricing model
based on smart metering and demand-side management. Energy, 46:
221–230, 2012.
[7] FERC. Staff report.: Assessment of demand response and advanced
metering. URL http://www.FERC.gov.
[8] A. Ghasemi, S. S. Mortazavi, and E. Mashhour. Integration of nodal
hourly pricing in day-ahead sdc (smart distribution company) optimization
framework to effectively activate demand response. Energy, 86:649–660, 2015.
[9] S. Golshannavaz, S. Afsharnia, and F. Aminifar. Smart distribution
grid: Optimal day-ahead scheduling with reconfigurable topology. IEEE
Transactions on Smart Grid, 5(5):2402–2411, 2014.
[10] R. Jabbari-Sabet, S. M. Moghaddas-Tafreshi, and S. S. Mirhoseini. Microgrid
operation and management using probabilistic reconfiguration
and unit commitment. International Journal of Electrical Power & Energy
Systems, 75:328–336, 2016.
[11] A. Kumar, S. C. Srivastava, and S. N. Singh. A zonal congestion management
approach using real and reactive power rescheduling. IEEE
Transactions on Power Systems, 19(1):554–562, 2004.
[12] M. Moeini-Aghtaie, A. Abbaspour, and M. Fotuhi-Firuzabad. Incorporating
large-scale distant wind farms in probabilistic transmission expansion
planning—part i: Theory and algorithm. IEEE Transactions
on Power Systems, 27(3):1585–1593, 2012.
[13] B. Moradzadeh and K. Tomsovic. Mixed integer programming-based
reconfiguration of a distribution system with battery storage. In Proceedings
of North American Power Symposium, Champaign, IL, USA,
sep 2012.
[14] L. Nikonowicz and J. Milewski. Virtual power plants - general review:
structure, application and optimization. Journal of Power Technologies,
92(3):135–149, 2012.
[15] M. Parastegari, R. A. Hooshmand, A. Khodabakhshian, and A. H.
Zare. Joint operation of wind farm, photovoltaic, pump-storage and
energy storage devices in energy and reserve markets. International
Journal of Electrical Power & Energy Systems, 64:275–284, 2015.
[16] S. Rahimi, Niknam T., and F. Fallahi. A new approach based on benders
decomposition for unit commitment problem. World Applied Science
Journal, 6(12):1665–1672, 2009.
[17] A. Safdarian, M. Fotuhi-Firuzabad, and M. Lehtonen. Integration of
price-based demand response in DisCos’ short-term decision model.
IEEE Transactions on Smart Grid, 5(5):2235–2245, 2014.
[18] F. C. Schweppe, M. C. Caramanis, R. D. Tabors, and R. E. Bohn. Spot
pricing of electricity, 1989.
[19] M. Shahidehpour and Y. Fu. Benders decomposition: applying benders
decomposition to power systems. IEEE Power and Energy Magazine,
3(2):1–2, 2005.
[20] T. Sousa, H. Morais, Z. Vale, P. Faria, and J. Soares. Intelligent energy
resource management considering vehicle-to-grid: A simulated
annealing approach. IEEE Transactions on Smart Grid, 3(1):535–542,
2012.
[21] G. Verbic and C. A. Canizares. Probabilistic optimal power flow in electricity
markets based on a two-point estimate method. IEEE Transactions
on Power Systems, 21:1–11, 2006.
[2] Ontario energy board (oeb). regulated price plan manual.
URL http://www.ontarioenergyboard.ca/OEB/Document
s/EB-2004-0205/RPP-Manual pdf.
[3] http://www.ee.washington.edu/research/pstca/pf30/pg_tca30bus.html.
[4] H. A. Aalami, M. ParsaMoghaddam, and G. R. Yousefi. Demand response
modeling considering interruptible/curtailable loads and capacity
market programs. Applied Ener, 87:243–250, 2010.
[5] G. Angevine and D. Hrytzak-Lieffers. Ontario industrial electricity demand
responsiveness to price. The Fraser Institute, 2007.
[6] M. Doostizadeh and H. Ghasemi. A day-ahead electricity pricing model
based on smart metering and demand-side management. Energy, 46:
221–230, 2012.
[7] FERC. Staff report.: Assessment of demand response and advanced
metering. URL http://www.FERC.gov.
[8] A. Ghasemi, S. S. Mortazavi, and E. Mashhour. Integration of nodal
hourly pricing in day-ahead sdc (smart distribution company) optimization
framework to effectively activate demand response. Energy, 86:649–660, 2015.
[9] S. Golshannavaz, S. Afsharnia, and F. Aminifar. Smart distribution
grid: Optimal day-ahead scheduling with reconfigurable topology. IEEE
Transactions on Smart Grid, 5(5):2402–2411, 2014.
[10] R. Jabbari-Sabet, S. M. Moghaddas-Tafreshi, and S. S. Mirhoseini. Microgrid
operation and management using probabilistic reconfiguration
and unit commitment. International Journal of Electrical Power & Energy
Systems, 75:328–336, 2016.
[11] A. Kumar, S. C. Srivastava, and S. N. Singh. A zonal congestion management
approach using real and reactive power rescheduling. IEEE
Transactions on Power Systems, 19(1):554–562, 2004.
[12] M. Moeini-Aghtaie, A. Abbaspour, and M. Fotuhi-Firuzabad. Incorporating
large-scale distant wind farms in probabilistic transmission expansion
planning—part i: Theory and algorithm. IEEE Transactions
on Power Systems, 27(3):1585–1593, 2012.
[13] B. Moradzadeh and K. Tomsovic. Mixed integer programming-based
reconfiguration of a distribution system with battery storage. In Proceedings
of North American Power Symposium, Champaign, IL, USA,
sep 2012.
[14] L. Nikonowicz and J. Milewski. Virtual power plants - general review:
structure, application and optimization. Journal of Power Technologies,
92(3):135–149, 2012.
[15] M. Parastegari, R. A. Hooshmand, A. Khodabakhshian, and A. H.
Zare. Joint operation of wind farm, photovoltaic, pump-storage and
energy storage devices in energy and reserve markets. International
Journal of Electrical Power & Energy Systems, 64:275–284, 2015.
[16] S. Rahimi, Niknam T., and F. Fallahi. A new approach based on benders
decomposition for unit commitment problem. World Applied Science
Journal, 6(12):1665–1672, 2009.
[17] A. Safdarian, M. Fotuhi-Firuzabad, and M. Lehtonen. Integration of
price-based demand response in DisCos’ short-term decision model.
IEEE Transactions on Smart Grid, 5(5):2235–2245, 2014.
[18] F. C. Schweppe, M. C. Caramanis, R. D. Tabors, and R. E. Bohn. Spot
pricing of electricity, 1989.
[19] M. Shahidehpour and Y. Fu. Benders decomposition: applying benders
decomposition to power systems. IEEE Power and Energy Magazine,
3(2):1–2, 2005.
[20] T. Sousa, H. Morais, Z. Vale, P. Faria, and J. Soares. Intelligent energy
resource management considering vehicle-to-grid: A simulated
annealing approach. IEEE Transactions on Smart Grid, 3(1):535–542,
2012.
[21] G. Verbic and C. A. Canizares. Probabilistic optimal power flow in electricity
markets based on a two-point estimate method. IEEE Transactions
on Power Systems, 21:1–11, 2006.
Published
2016-10-29
How to Cite
GHASEMI, Ahmad.
Optimal reconfiguration and scheduling of a smart distribution network with uncertain renewables and price-responsive demand.
Journal of Power Technologies, [S.l.], v. 96, n. 3, p. 183--193, oct. 2016.
ISSN 2083-4195.
Available at: <https://papers.itc.pw.edu.pl/index.php/JPT/article/view/864>. Date accessed: 18 apr. 2024.
Issue
Section
Renewable and Sustainable Energy
Keywords
Reconfiguration; Smart distribution network (SDN); Demand response; Retail pricing; Wind power generation; Uncertainty
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
