Peak-Load Energy Management by Direct Load Control Contracts

Published Online:https://doi.org/10.1287/mnsc.2022.4493

References

  • Aalami HA, Moghaddam MP, Yousefi GR (2010a) Demand response modeling considering interruptible/curtailable loads and capacity market programs. Appl. Energy 87(1):243–250.CrossrefGoogle Scholar
  • Aalami HA, Moghaddam MP, Yousefi GR (2010b) Modeling and prioritizing demand response programs in power markets. Electric Power Systems Res. 80(4):426–435.CrossrefGoogle Scholar
  • Adelman D (2007) Dynamic bid prices in revenue management. Oper. Res. 55(4):647–661.LinkGoogle Scholar
  • Aghaei J, Alizadeh MI (2013) Demand response in smart electricity grids equipped with renewable energy sources: A review. Renewable Sustainable Energy Rev. 18:64–72.CrossrefGoogle Scholar
  • Agrawal V, Yucel Ş (2020) Design of electricity demand-response programs. Preprint, submitted September 15, https://dx.doi.org/10.2139/ssrn.3659574.Google Scholar
  • Ahmed S, Sahinidis NV (2003) An approximation scheme for stochastic integer programs arising in capacity expansion. Oper. Res. 51(3):461–471.LinkGoogle Scholar
  • Alizadeh M, Li X, Wang Z, Scaglione A, Melton R (2012) Demand side management in the smart grid: Information processing for the power switch. IEEE Signal Processing Magazine 29(5):55–67.CrossrefGoogle Scholar
  • Alizamir S, de Vericourt F, Sun P (2016) Efficient feed-in-tariff policies for renewable energy technologies. Oper. Res. 64(1):52–66.LinkGoogle Scholar
  • Alizamir S, Farajbakhsh F, Wang S (2020) Electricity pricing with limited consumer response. Working Paper, Yale School of Management, New Haven, CT.Google Scholar
  • Baldick R, Kolos S, Tompaidis S (2006) Interruptible electricity contracts from an electricity retailer’s point of view: Valuation and optimal interruption. Oper. Res. 54(4):627–642.LinkGoogle Scholar
  • Bean JC, Birge JR, Smith RL (1987) Aggregation in dynamic programming. Oper. Res. 35(2):215–220.LinkGoogle Scholar
  • Bertsekas DP (2012) Dynamic Programming and Optimal Control, vol. 1 (Athena Scientific, Belmont, MA).Google Scholar
  • Bertsekas DP, Castanon D (1989) Adaptive aggregation methods for infinite horizon dynamic programming. IEEE Trans. Automatic Control 34(6):589–598.CrossrefGoogle Scholar
  • Bertsimas D, Tsitsiklis JN (1997) Introduction to Linear Optimization, vol. 6 (Athena Scientific, Belmont, MA), 479–530.Google Scholar
  • Birge JR, Louveaux F (1997) Introduction to Stochastic Programming (Springer, New York).Google Scholar
  • Black JW, Tyagi R, Massey JS, Williams JD (2012) U.S. Patent application no. 12/957,299.Google Scholar
  • Callaway DS, Hiskens IA (2010) Achieving controllability of electric loads. Proc. IEEE 99(1):184–199.CrossrefGoogle Scholar
  • Chen C, Kishore S, Snyder LV (2011) An innovative RTP-based residential power scheduling scheme for smart grids. IEEE Internat. Conf. Acoustics Speech Signal Processing (ICASSP), 5956–5959.Google Scholar
  • Cooper WL (2002) Asymptotic behavior of an allocation policy for revenue management. Oper. Res. 50(4):720–727.LinkGoogle Scholar
  • De Meulemeester B (2014) Capacity Payments: Expensive Solution for a Non-Existing Problem (EnergyPost).Google Scholar
  • Deng R, Yang Z, Chow MY, Chen J (2015) A survey on demand response in smart grids: Mathematical models and approaches. IEEE Trans. Indust. Inform. 11(3):570–582.CrossrefGoogle Scholar
  • Doostizadeh M, Ghasemi H (2012) A day-ahead electricity pricing model based on smart metering and demand-side management. Energy 46(1):221–230.CrossrefGoogle Scholar
  • Ericson T (2009) Direct load control of residential water heaters. Energy Policy 37(9):3502–3512.CrossrefGoogle Scholar
  • Fan Z, Kulkarni P, Gormus S, Efthymiou C, Kalogridis G, Sooriyabandara M, Zhu Z, Lambotharan S, Chin WH (2013) Smart grid communications: Overview of research challenges, solutions, and standardization activities. IEEE Comm. Surveys Tutorials 15(1):21–38.CrossrefGoogle Scholar
  • Faruqui A, Hledik R, Tsoukalis J (2009) The power of dynamic pricing. Electricity J. 22(3):42–56.CrossrefGoogle Scholar
  • Federal Energy Regulatory Commission (2020) Assessment of demand response and advance metering. Staff report, Federal Energy Regulatory Commission.Google Scholar
  • Garey MR, Johnson DS (1979) Computers and Intractability: A Guide to the Theory of NP-Completeness (WH Freeman & Co., San Francisco).Google Scholar
  • Geoffrion AM (1977) Aggregation theory and its application to modeling in mathematical programming. Working Paper No. 278, Western Management Science UCLA, Los Angeles.Google Scholar
  • Haider HT, See OH, Elmenreich W (2016) A review of residential demand response of smart grid. Renewable Sustainable Energy Rev. 59:166–178.CrossrefGoogle Scholar
  • Jasin S, Kumar S (2012) A re-solving heuristic with bounded revenue loss for network revenue management with customer choice. Math. Oper. Res. 37(2):313–345.LinkGoogle Scholar
  • Kamat R, Oren SS (2002) Exotic options for interruptible electricity supply contracts. Oper. Res. 50(5):835–850.LinkGoogle Scholar
  • Kostkova K, Omelina L, Kycina P, Jamrich P (2013) An introduction to load management. Electric Power Systems Res. 95:184–191.CrossrefGoogle Scholar
  • Lesage-Landry A (2019) Online optimization for demand response. Unpublished dissertation, University of Toronto, Toronto.Google Scholar
  • Liu Q, Van Ryzin G (2008) On the choice-based linear programming model for network revenue management. Manufacturing Service Oper. Management 10(2):288–310.LinkGoogle Scholar
  • Mathieu JL, Vayá MG, Andersson G (2013) Uncertainty in the flexibility of aggregations of demand response resources. IECON 2013 39th Annual Conf. IEEE Indust. Electronics Soc. (IEEE), 8052–8057.Google Scholar
  • Mendelssohn R (1982) An iterative aggregation procedure for Markov decision processes. Oper. Res. 30(1):62–73.LinkGoogle Scholar
  • Michigan Public Service Commission (2015) Common demand response practices and program designs. Accessed December 2016, www.michigan.gov.Google Scholar
  • Next Kraftwerke (2016) Merit order curve. Accessed December 2016, www.next-kraftwerke.be.Google Scholar
  • Oren SS, Smith SA (1992) Design and management of curtailable electricity service to reduce annual peaks. Oper. Res. 40(2):213–228.LinkGoogle Scholar
  • Peura H, Bunn DW (2015) Dynamic pricing of peak production. Oper. Res. 63(6):1262–1279.LinkGoogle Scholar
  • Posner B (2015) The fundamentals of electricity markets. www.e-education.psu.edu.Google Scholar
  • U.S. Department of Energy (2006) Benefits of demand response in electricity markets and recommendations for achieving them. Washington, DC.Google Scholar
  • Rogers DF, Plante RD, Wong RT, Evans JR (1991) Aggregation and disaggregation techniques and methodology in optimization. Oper. Res. 39(4):553–582.LinkGoogle Scholar
  • Shoreh MH, Siano P, Shafie-khah M, Loia V, Catalao JP (2016) A survey of industrial applications of demand response. Electric Power Systems Res. 141:31–49.CrossrefGoogle Scholar
  • Siano P (2014) Demand response and smart grids—A survey. Renewable Sustainable Energy Rev. 30:461–478.CrossrefGoogle Scholar
  • Taylor B, Taylor C (2015) Demand Response: Managing Electric Power Peak Load Shortages with Market Mechanisms (Regulatory Assistance Project, Washington, DC).Google Scholar
  • Taylor JA, Mathieu JL (2015) Uncertainty in demand response—Identification, estimation, and learning. The Operations Research Revolution (Informs), 56–70.LinkGoogle Scholar
  • Tyagi R, Black JW (2011) U.S. Patent application no. 12/790,655.Google Scholar
  • Van Roy B (2006) Performance loss bounds for approximate value iteration with state aggregation. Math. Oper. Res. 31(2):234–244.LinkGoogle Scholar
  • Vardakas JS, Zorba N, Verikoukis CV (2015) A survey on demand response programs in smart grids: Pricing methods and optimization algorithms. IEEE Comm. Surveys Tutorials 17(1):152–178.CrossrefGoogle Scholar
  • Whitt W (1978) Approximations of dynamic programs, I. Math. Oper. Res. 3(3):231–243.LinkGoogle Scholar
  • Wissner M (2011) The smart grid—A saucerful of secrets? Appl. Energy 88(7):2509–2518.CrossrefGoogle Scholar
  • Xu R, Wunsch D (2005) Survey of clustering algorithms. IEEE Trans. Neural Networks 16(3):645–678.CrossrefGoogle Scholar
  • Zhang D, Adelman D (2009) An approximate dynamic programming approach to network revenue management with customer choice. Transportation Sci. 43(3):381–394.LinkGoogle Scholar
  • Zipkin PH (1980a) Bounds for row-aggregation in linear programming. Oper. Res. 28(4):903–916.LinkGoogle Scholar
  • Zipkin PH (1980b) Bounds on the effect of aggregating variables in linear programs. Oper. Res. 28(2):403–418.LinkGoogle Scholar
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