Benefits of demands control in a smart-grid to compensate the volatility of non-conventional energies

Keywords: Wind-power, smart-cities, short-term dispatch

Abstract

Uruguay is a leader in the usage of renewable energies, getting 96% of its electricity from an assorted assemble of such sources with an increasing percentage of non-conventional energies, of wind power mostly. As clean and financially stable as they are, non-conventional energies have weaknesses. Unlike thermic and most hydro-sources, wind and solar energies are not controllable,are intermittent and uncertain some hours ahead, complicating the short-term operation and maintenance of electrical systems. This work explores how to use smart-grids capabilities to adjust electricity demand as a hedge against novel short-position risks in the Uruguayan electricity market coming from the volatility of non-conventional renewables. This approach uses combinatorial optimization dispatch models to quantitatively assess benefits resulting from having demand control. Results show that for the Uruguayan context, the benefits are not only due to savings in production costs (generation). Smart-grid optimal dispatch-schedules are also less stressing regarding the operation of the grid itself.

Downloads

Download data is not yet available.

Author Biography

C. Risso, Universidad de la República
Facultad de Ingeniería

References

C. Risso, “Using smart-grids capabilities as a natural hedge against novel risks coming from non-conventional renewable electricity generation,” in Ibero-American Congress on Information Management and Big Data, Soria, Spain, 2018.

REN21, “Renewables 2018 global status report,” REN21 Community, Paris, France, Tech. Rep., 2018.

R. Karki and R. Billinton, “Cost-effective wind energy utilization for reliable power supply,” IEEE Transactions on Energy Conversion, vol. 19, no. 2, pp. 435–440, Jun. 2004.

J. M. Morales, A. J. Conejo, H. Madsen, P. Pinson, and M. Zugno, Integrating Renewables in Electricity Markets, 1st ed. New York, USA: Springer US, 2014.

M. Joosa and I. Staffell, “Short-term integration costs of variable renewable energy: Wind curtailment and balancing in britain and germany,” Renewable and Sustainable Energy Reviews, vol. 86, pp. 45–65, Apr. 2018.

N. Li, L. Chen, and S. H. Low, “Optimal demand response based on utility maximization in power networks,” in 2011 IEEE Power and Energy Society General Meeting, Jul. 2011, pp. 1–8.

A. Mohsenian-Rad and A. Leon-Garcia, “Optimal residential load control with price prediction in real-time electricity pricing environments,” IEEE Transactions on Smart Grid, vol. 1, no. 2, pp. 120–133, Sep. 2010.

F. Paganini, P. Belzarena, and P. Monzón, “Decision making in forward power markets with supply and demand uncertainty,” in 2014 48th Annual Conference on Information Sciences and Systems (CISS), Mar. 2014, pp. 1–6.

W. Jeon, A. J. Lamadrid, J. Y. Mo, and T. D. Mount, “Using deferrable demand in a smart grid to reduce the cost of electricity for customers,” Journal of Regulatory Economics, vol. 47, no. 3, pp. 239–272, Jun. 2015.

J. Y. Mo and W. Jeon, “How does energy storage increase the efficiency of an electricity market with integrated wind and solar power generation?—A case study of Korea,” Sustainability, vol. 8, no. 10, 2017.

L. Jiang and S. Low, “Multi-period optimal energy procurement and demand response in smart grid with uncertain supply,” in 2011 50th IEEE Conference on Decision and Control and European Control Conference, Dec. 2011, pp. 4348–4353.

I. Atzeni, L. G. Ordóñez, G. Scutari, D. P. Palomar, and J. R. Fonollosa, “Demand-side management via distributed energy generation and storage optimization,” IEEE Transactions on Smart Grid, vol. 4, no. 2, pp. 866–876, Jun. 2013.

Y. Wu, V. K. N. Lau, D. H. K. Tsang, L. P. Qian, and L. Meng, “Optimal energy scheduling for residential smart grid with centralized renewable energy source,” IEEE Systems Journal, vol. 8, no. 2, pp. 562–576, Jun. 2014.

S. Montes de Oca, P. Belzarena, and P. Monzón, “Optimal demand response based on time-correlated utility in forward power markets,” in 2015 IEEE PES Innovative Smart Grid Technologies Latin America (ISGT LATAM), Oct. 2015, pp. 597–602.

C. Risso and G. Guerberoff. (2018, May. 10) Nonparametric optimization of short-term confidence bands for wind power generation. Online.. Available: https://arxiv.org/abs/1805.04474v1

S. de Mello, G. Cazes, and A. Gutierrez, “Operational wind energy forecast with power assimilation,” 14th International Conference on Wind Engineering, Porto Alegre, Brazil, 2015.

Published
2019-08-23