Optimal Virtual Power Plant Dispatching Approach

Article Preview

Abstract:

As the integrator of energy resources (DERs), a virtual power plant (VPP) would be able to control the amount of the power access to the distribution transformers such that energy efficiency can be improved. Battery energy storage system (BESS) and demand response (DR) as DERs can entrust the VPP with certain controllability to regulate the power supply of the distribution system. This paper aims to maximize the benefit of the supplied powers over the 24 hours under VPP operation. Combining an iterative dynamic programming optimal BESS schedule approach and a PSO-based DR scheme optimization approach, an optimal VPP operational method is proposed to minimize the total electricity cost with respect to the power supply limit of the distribution transformers and the system security constraints, especially, within the peak load hours. With the TOU rate given each hour, test results had confirmed the validity of the proposed method with the obviously decreased power supply in each peak-load hours and the largely reduced electricity cost accordingly.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

511-515

Citation:

Online since:

June 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] Lombardi P, et. Al. Multi-crieria optimization of an energy storage system within a virtual power plant architecture. IEEE PES 2011, pp.1-6.

Google Scholar

[2] Pudjianto D, Ramsay C, Strbac G. Virtual power plant and system integration of distribution energy resources. IET Renew. Power Gen., 2007, 1, (1), pp.10-16.

DOI: 10.1049/iet-rpg:20060023

Google Scholar

[3] Pudjianto D, Ramsay C, Strbac G. Microgrids and virtual power plants: concepts to support the integration of distributed energy resources. Proc. of the institution of mechanical engineers, part A: Journal of Power and Energy, vol. 222, no. 7, Nov. 2008, pp.731-714.

DOI: 10.1243/09576509jpe556

Google Scholar

[4] Mashhour E, Moghaddas-Tafreshi SM. Trading models for aggregating distributed energy resources into virtual power plant. Proc. 2th Int. Conf. on Adaptive Science & Tech. Accra, Ghana, Dec. (2009).

DOI: 10.1109/icastech.2009.5409692

Google Scholar

[5] Mashhour E. Moghaddas-Tafreshi SM. Bidding strategy of virtual power plant for participating in energy and spinning reserve markets- part I: problem formulation. IEEE Trans. Power Syst., vol. 26, no. 2, May 2011, p.949–956.

DOI: 10.1109/tpwrs.2010.2070884

Google Scholar

[6] Mashhour E. Moghaddas-Tafreshi SM. Bidding strategy of virtual power plant for participating in energy and spinning reserve markets-Part II: Numerical Analysis, " IEEE Trans. Power Syst., vol. 26, no. 2, May 2011, p.957–964.

DOI: 10.1109/tpwrs.2010.2070883

Google Scholar

[7] Raab AF, et. al. Virtual power plant control concepts with electric vehicles. ISAP, 2011 16th International Conference, Sept. 2011, pp.1-6.

Google Scholar

[8] Assessment of demand response and advanced metering. Federal Energy Regulatory Commission, Feb. 2011 [Online]. Available: http: /www. ferc. gov/legal/staff-reports/2010-dr-report. pdf.

Google Scholar

[9] Shao S, Pipattanasomporn M, Rahman S. Demand response as a load shaping tool in an intelligent grid with electric vehicles. IEEE Trans. Smart Grid, vol. 2, no. 4, Dec. 2011, p.624–630.

DOI: 10.1109/tsg.2011.2164583

Google Scholar

[10] Conejo AJ, Morales JM, Baringo L. Real-Time demand response model. IEEE Trans. Smart Grid, vol. 1, no. 3, Dec. 2010, p.236–242.

DOI: 10.1109/tsg.2010.2078843

Google Scholar

[11] Styczynski Z, Lombardi P, et. al. Electric energy storage and its tasks in the integration of wide scale renewable energy resource. Cigre-PES Symposium, Calgary, July (2009).

Google Scholar

[12] Tanaka K, Yoshinaga J, Kobayashi N. The sodium-sulfur battery for utility-scale applications. Cigre Session, Paris, Aug. (2008).

Google Scholar