- Volume 9 Issue 3
In a demand response (DR) market run by independent system operators (ISOs), load aggregators are important market participants who aggregate small retail customers through various DR programs. A load aggregator can minimize the allocation cost by efficiently allocating its demand response resources (DRRs) considering retail customers' characteristics. However, the uncertain response behaviors of retail customers can influence the allocation strategy of its DRRs, increasing the economic risk of DRR allocation. This paper presents a risk-based DRR allocation method for the load aggregator that takes into account not only the physical characteristics of retail customers but also the risk due to the associated response uncertainties. In the paper, a conditional value-at-risk (CVaR) is applied to deal with the risk due to response uncertainties. Numerical results are presented to illustrate the effectiveness of the proposed method.
Demand Response (DR);Demand Response Resource (DRR);Load Aggregator;Conditional Value-at-Risk (CVaR)
- Federal Energy Regulatory Commission, "Assessment of Demand Response and Advanced Metering: Staff Report," Dec. 2012.
- K. Dietrich, J. M. Latorre, L. Olmos, and A. Ramos, "Demand Response in an Isolated System with High Wind Integration," IEEE Trans. Power Syst., vol. 27, no. 1, pp. 20-29, Feb. 2012. https://doi.org/10.1109/TPWRS.2011.2159252
- J. Soares, H. Morais, T. Sousa, and P. Faria, "Day- Ahead Resource Scheduling Including Demand Response for Electric Vehicles," IEEE Trans. Smart Grid, vol. 4, no. 1, pp. 596-605, Mar. 2013. https://doi.org/10.1109/TSG.2012.2235865
- P. Yi, X. Dong, A. Iwayemi, C. Zhou, and S. Li, "Real-Time Opportunistic Scheduling for Residential Demand Response," IEEE Trans. Smart Grid, vol. 4, no. 1, pp. 227-234, Mar. 2013. https://doi.org/10.1109/TSG.2012.2225155
- A. Khodaei, M. Shahidehpour, and S. Bahramirad, "SCUC with Hourly Demand Response Considering Intertemporal Load Characteristics," IEEE Trans. Smart Grid, vol. 2, no. 3, pp. 564-571, Sep. 2011. https://doi.org/10.1109/TSG.2011.2157181
- J. Y. Joo and M. Ilic, "Distributed Multi-Temporal Risk Management Approach to Designing Dynamic Pricing," IEEE Power & Energy Society General Meeting, California, USA, July 2012.
- S. Shao and M. Pipattanasomporn, "An Approach for Demand Response to Alleviate Power System Stress Conditions," IEEE Power & Energy Society General Meeting, Michigan, USA, July 2011.
- S. V. Bruno and C. Sagastizabal, "Optimization of Real Asset Portfolio Using a Coherent Risk Measure: Application to Oil and Energy Industries," Optimization Online, pp. 1-8. Nov. 2008.
- Korea Power Exchange (KPX), "Demand Response Market Operation Rules," Ministry of Knowledge Economy in Korea, Jan. 2013.
- Y. Fu, M. Shahidehdour, and Z. Li, "Security- Constrained Unit Commitment with AC Constraints," IEEE Trans. Power Syst., vol. 20, no. 3, pp. 1538-1550, Aug. 2005. https://doi.org/10.1109/TPWRS.2005.854375
- M. Shahidehpour and Y. Fu, "Benders decomposition," IEEE Power Energy Mag., vol. 3, no. 2, pp. 20-21, Mar. 2005.
- K. Cuthbertson and D. Nitzsche, Financial Engineering: Derivatives and Risk Management, Wiley, 2004, pp. 559-601.
- M. Mazaheri, "Risk Budgeting Using Expected Shortfall (CVaR): An Overview," SSRN, Jun. 2008.
- John C. Hull, Options, Futures, and Other Derivatives, vol. 6, New Jersey: PEARSON, 2006, pp. 435-437.
- C. I. Fabrian, "Handling CVaR Objectives and Constraints in Two-Stage Stochastic Models," Eur. J. Oper. Res., vol. 191, no. 3, pp. 888-911, Dec. 2008. https://doi.org/10.1016/j.ejor.2007.02.052
- Y. H. Huang, "A Comparison of Value at Risk Approaches and a New Method with Extreme Value Theory and Kernel Estimator," Ph.D. dissertation, Dept. of Economics, CUNY, New York, USA, 2006.
- R. S. Tsay, Analysis of Financial Time Series (3rd ed.), Wiley, 2010, pp. 333-338.