A Dynamic Penalty Cost Allocation Based Uncertain Wind Energy Scheduling in Smart Grid

Srikanth Konda, Lokesh Panwar, B K Panigrahi, Rajesh Kumar, Sai Goutham

Abstract


The intermittent nature of wind energy conversion presents a risk to the Independent System Operator (ISO) in a real time electricity market. Consequently, there is a need to appropriately incorporate this risk in the wind energy scheduling paradigm. In the present work, the intermittency associated risk has been modeled as part of a cost optimization problem for the ISO in real time. A model to minimize the risk has also been proposed using various cost models that use dynamic risk aversion costs and reflect the market and system operating conditions. The two dynamic penalty cost/risk models included are, rescheduling cost and contractual compensation cost for wind energy deviation. The results obtained are compared with those from the deterministic model. The proposed approach is simulated on the IEEE 30 bus system and the findings from the proposed approach for wind energy scheduling lead to a low operational cost to the ISO in the real time market considered in the study. Among other observations, the consideration of uncertainty in Day-Ahead market leads to increase in cost savings of ISO with increase in wind uncertainty, but a corresponding reduction in the scheduled wind energy in the same market.


Keywords


Day ahead market; real time market; wind energy; independent system operator; market clearing price; spot market price

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References


X. Liu and W. Xu, “Economic load dispatch constrained by wind power availability: A here and-now approach,†IEEE Trans. Sustain. Energy,vol. 1, no. 1, pp. 2–9, Apr. 2010.

J. P. S. Catalão, H.M. I. Pousinho, and V.M. F.Mendes, “Optimal offering strategies for wind power producers considering uncertainty and risk,†IEEE Syst. J., vol. 6, no. 2, pp. 270–277, Jun. 2012.

J. Zhang, J. D. Fuller, and S. Elhedhli, “A stochastic programming model for a day-ahead electricity market with real-time reserve shortage pricing,†IEEE Trans. Power Syst., vol. 25, no. 2, pp. 703–713, May 2010.

Z. Song, L. Goel, and P. Wang, “Optimal spinning reserve allocation in deregulated power systems,†Proc. Inst. Elect. Eng.—Gener. Transm. Distrib., vol. 152, no. 4, pp. 483–488, Jul. 2005.

J. M. Morales, A. J. Conejo, and J. P. Ruiz, “Economic valuation of reserves in power systems with high penetration of wind power,†IEEETrans. Power Syst., vol. 24, no. 2, pp. 900–910, May 2009.

H. B. Gooi, D. P. Mendes, K. R. W. Bell, and D. S. Kirschen, “Optimal scheduling of spinning reserve,†IEEE Trans. Power Syst., vol. 14, no. 4, pp. 1485–1492, Nov. 1999.

Das, D.; Wollenberg, B.F., "Risk assessment of generators bidding in day-ahead market," IEEE Transactions on Power Systems, vol.20, no.1, pp.416-424, Feb. 2005.

Rau, N.S.; Fei Zeng, "Adequacy and responsibility of locational generation and transmission-optimization procedures," IEEE Transactions on Power Systems, vol.19, no.4, pp.2093-2101, Nov. 2004

Peter Sorknæs, Henrik Lund, Anders N. Andersen, Future power market and sustainable energy solutions – The treatment of uncertainties in the daily operation of combined heat and power plants, Applied Energy, Volume 144, Pages 129-138, 15 April 2015.

Bessembinder, H., & Lemmon, M., “Equilibrium pricing and optimal hedging in electricity forward markets. Journal of Financeâ€, Volume 57, Issue 3, Pages 1347–1382, March 2002.

Longstaff, F. A., & Wang, A. W. “Electricity forward prices: A high-frequency empirical analysis. The Journal of Financeâ€, Volume 59, Issue 4, Pages 1877–1900, June 2004.

Hogan, W. W., C. Cullen Hitt, et al. Governance Structures for an Independent System Operator (ISO). Harvard Electricity Policy Group Background Paper. Cambridge, MA, Center for Business and Government, Harvard University, 1996.

Shamsollahi, P.; Van Acker, V., "Functional requirements for Southwest Power Pool Energy Imbalance market dispatch," IEEE Bucharest PowerTech., pp.1-6, June 28-July 2 2009.

Michael G. Pollitt, Lessons from the History of Independent System Operators in the Energy Sector, with applications to the Water Sector, Cambridge Working Paper in Economics, Electricity Policy Research Group, August 2011.

G. Tina, S. Gagliano, S. Raiti, Hybrid solar/wind power system probabilistic modelling for long-term performance assessment, Solar Energy, Volume 80, Issue 5, Pages 578-588, May 2006.

A.K. Azad, M.G. Rasul, M.M. Alam, S.M. Ameer Uddin, Sukanta Kumar Mondal, Analysis of Wind Energy Conversion System Using Weibull Distribution, Procedia Engineering, Volume 90, Pages 725-732, 2014.

Azza A. ElDesouky, Security constrained generation scheduling for grids incorporating wind, Photovoltaic and thermal power, Electric Power Systems Research, Volume 116, Pages 284-292, November 2014.

S. Surender Reddy, P.R. Bijwe, A.R. Abhyankar, Optimum day-ahead clearing of energy and reserve markets with wind power generation using anticipated real-time adjustment costs, International Journal of Electrical Power & Energy Systems, Volume 71, Pages 242-253, October 2015.

Ferrero, R.W., Shahidehpour, S.M., Ramesh, V.C., â€Transaction analysis in deregulated power systems using game theoryâ€, IEEE Transactions on Power Systems, Vol. 12, No. 3, pp. 1340-1347, Aug 1997.

[online]Available:http://www:suzlon:com/pdf/S97- ProductbrochureF V 5:pdf

Tian-Pau Chang, Feng-Jiao Liu, Hong-Hsi Ko, Shih-Ping Cheng, Li- Chung Sun, Shye-Chorng Kuo, “Comparative analysis on power curve models of wind turbine generator in estimating capacity factorâ€, Energy, Volume 73, Pages 88-95, 14 August 2014.




DOI (PDF): https://doi.org/10.20508/ijrer.v7i1.5440.g6995

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