Operations Research Helps the Optimal Bidding of Virtual Power Plants

Published Online:https://doi.org/10.1287/inte.2022.1120

References

  • ACEEE (2021) Distributed energy resources. Accessed August 30, 2021, https://www.aceee.org/topic/distributed-energy-resources.Google Scholar
  • AEMO (2020) Small generation aggregators in the NEM. Technical report, Melbourne, Australia.Google Scholar
  • Aghabozorgi S, Shirkhorshidi AS, Wah TY (2015) Time-series clustering: A decade review. Inform. Systems 53:16–38.Google Scholar
  • Allied Market Research (2020) Virtual power plant market by technology and by end user: Global opportunity analysis and industry forecast, 2020–2027. Technical report, Research and Markets, Dublin, Ireland.Google Scholar
  • Baringo A, Baringo L, Arroyo JM (2018) Day-ahead self-scheduling of a virtual power plant in energy and reserve electricity markets under uncertainty. IEEE Trans. Power Systems 34(3):1881–1894.Google Scholar
  • Beraldi P, Violi A, Carrozzino G, Bruni ME (2018) A stochastic programming approach for the optimal management of aggregated distributed energy resources. Comput. Oper. Res. 96:200–212.Google Scholar
  • Bertoldi P, Zancanella P, Boza-Kiss B (2016) Demand response status in EU member states. Technical report, Joint Research Centre, Brussels, Belgium.Google Scholar
  • Box GE, Jenkins GM, Reinsel GC, Ljung GM (2015) Time Series Analysis: Forecasting and Control (John Wiley & Sons, Hoboken, NJ).Google Scholar
  • Burtch G, Hong Y, Bapna R, Griskevicius V (2018) Stimulating online reviews by combining financial incentives and social norms. Management Sci. 64(5):2065–2082.LinkGoogle Scholar
  • Cachon GP, Daniels KM, Lobel R (2017) The role of surge pricing on a service platform with self-scheduling capacity. Manufacturing Service Oper. Management 19(3):368–384.LinkGoogle Scholar
  • CAISO (2016) Distributed energy resource provider participation guide with checklist version 1.0. Technical report, Folsom, CA.Google Scholar
  • Camm JD, Raturi AS, Tsubakitani S (1990) Cutting big M down to size. Interfaces 20(5):61–66.LinkGoogle Scholar
  • Campaigne C, Oren SS (2016) Firming renewable power with demand response: An end-to-end aggregator business model. J. Regulatory Econom. 50(1):1–37.Google Scholar
  • Daniels K, Lobel R (2014) Demand response in electricity markets: Voluntary and automated curtailment contracts. Preprint, submitted October 3, https://dx.doi.org/10.2139/ssrn.2505203.Google Scholar
  • Ding H, Pinson P, Hu Z, Wang J, Song Y (2017) Optimal offering and operating strategy for a large wind-storage system as a price maker. IEEE Trans. Power Systems 32(6):4904–4913.Google Scholar
  • Dupacová J, Gröwe-Kuska N, Römisch W (2000) Scenario Reduction in Stochastic Programming: An Approach Using Probability Metrics (Humboldt-Universit’´at zu Berlin, Mathematisch-Naturwissenschaftliche Fakult’´at II, Institut f’´ur Mathematik).Google Scholar
  • Fazlalipour P, Ehsan M, Mohammadi-Ivatloo B (2019) Risk-aware stochastic bidding strategy of renewable micro-grids in day-ahead and real-time markets. Energy 171:689–700.Google Scholar
  • Guda H, Subramanian U (2019) Your Uber is arriving: Managing on-demand workers through surge pricing, forecast communication, and worker incentives. Management Sci. 65(5):1995–2014.AbstractGoogle Scholar
  • Guidehouse-Insight (2016) Global demand response capacity is expected to grow to 144 GW in 2025 (Washington, DC). Accessed August 30, 2021, https://guidehouseinsights.com/news-and-views/global-demand-response-capacity-is-expected-to-grow-to-144-gw-in-2025.Google Scholar
  • Guidehouse-Insight (2019) Market data: Virtual power plants: Demand response, supply-side, and mixed asset VPP segment and regional forecasts for capacity, implementation spending, and market revenue. Technical report, Washington, DC.Google Scholar
  • Guidehouse-Insight (2020) Virtual power plant overview: Flexibility market analysis and forecasts, 2020–2029. Technical report, Washington, DC.Google Scholar
  • Heitsch H, Römisch W (2009) Scenario tree modeling for multistage stochastic programs. Math. Programming 118(2):371–406.Google Scholar
  • Henríquez R, Wenzel G, Olivares DE, Negrete-Pincetic M (2017) Participation of demand response aggregators in electricity markets: Optimal portfolio management. IEEE Trans. Smart Grid 9(5):4861–4871.Google Scholar
  • Høyland K, Wallace SW (2001) Generating scenario trees for multistage decision problems. Management Sci. 47(2):295–307.LinkGoogle Scholar
  • Høyland K, Kaut M, Wallace SW (2003) A heuristic for moment-matching scenario generation. Comput. Optim. Appl. 24(2–3):169–185.Google Scholar
  • Huang K, Ahmed S (2009) The value of multistage stochastic programming in capacity planning under uncertainty. Oper. Res. 57(4):893–904.LinkGoogle Scholar
  • IRENA (2019) Renewable energy statistics 2019. Accessed August 30, 2021, https://www.irena.org/publications/2019/Jul/Renewable-energy-statistics-2019.Google Scholar
  • Jia BB, Zhang ML (2020) Multi-dimensional classification via KNN feature augmentation. Pattern Recognition 106:107423.Google Scholar
  • Ju L, Tan Z, Yuan J, Tan Q, Li H, Dong F (2016) A bi-level stochastic scheduling optimization model for a virtual power plant connected to a wind–photovoltaic–energy storage system considering the uncertainty and demand response. Appl. Energy 171:184–199.Google Scholar
  • Kazempour SJ, Zareipour H (2014) Equilibria in an oligopolistic market with wind power production. IEEE Trans. Power Systems 29(2):686–697.Google Scholar
  • KPX (2021a) Power market statistics in 2020. Accessed August 30, 2021, http://epsis.kpx.or.kr/epsisnew/selectEkifBoardList.do?menuId=080401&boardId=040100.Google Scholar
  • KPX (2021b) Power market/new market operations performance in March 2021. Accessed August 30, 2021, https://www.kpx.or.kr/www/selectBbsNttView.do?key=100&bbsNo=8&nttNo=22396&searchCtgry=&searchCnd=all&searchKrwd=&pageIndex=2&integrDeptCode=.Google Scholar
  • Kuang L, Huang N, Hong Y, Yan Z (2019) Spillover effects of financial incentives on non-incentivized user engagement: Evidence from an online knowledge exchange platform. J. Management Inf. Systems 36(1):289–320.Google Scholar
  • Liang Z, Guo Y (2016) Robust optimization based bidding strategy for virtual power plants in electricity markets. Proc. IEEE Power and Energy Soc. General Meeting (IEEE, New Jersey), 1–5.Google Scholar
  • Liu M, Brynjolfsson E, Dowlatabadi J (2021) Do digital platforms reduce moral hazard? The case of Uber and taxis. Management Sci. 67(8):4665–4685.Google Scholar
  • Maranas CD, Zomorrodi AR (2016) Optimization Methods in Metabolic Networks (John Wiley & Sons, Hoboken, NJ).Google Scholar
  • Mashhour E, Moghaddas-Tafreshi SM (2010) Bidding strategy of virtual power plant for participating in energy and spinning reserve markets-part I: Problem formulation. IEEE Trans. Power Systems 26(2):949–956.Google Scholar
  • Morales-España G, Correa-Posada CM, Ramos A (2016) Tight and compact MIP formulation of configuration-based combined-cycle units. IEEE Trans. Power Systems 31(2):1350–1359.Google Scholar
  • Nekouei E, Alpcan T, Chattopadhyay D (2014) Game-theoretic frameworks for demand response in electricity markets. IEEE Trans. Smart Grid 6(2):748–758.Google Scholar
  • NYISO (2020) Nyiso demand response programs FAQs for prospective resources. Accessed August 30, 2021, https://www.nyiso.com/documents/20142/1398619/NYISO-Demand-Response-FAQs-for-Prospective-Resources.pdf/0377863d-84a6-de03-7486-f98e23f631e5.Google Scholar
  • Ottesen SØ, Tomasgard A, Fleten SE (2018) Multi market bidding strategies for demand side flexibility aggregators in electricity markets. Energy 149:120–134.Google Scholar
  • Pudjianto D, Ramsay C, Strbac G (2007) Virtual power plant and system integration of distributed energy resources. IET Renewable Power Generation 1(1):10–16.Google Scholar
  • Read J, Bielza C, Larrañaga P (2014) Multi-dimensional classification with super-classes. IEEE Trans. Knowledge Data Engrg. 26(7):1720–1733.Google Scholar
  • Shi L, Luo Y, Tu G (2014) Bidding strategy of microgrid with consideration of uncertainty for participating in power market. Internat. J. Electric Power Energy Systems 59:1–13.Google Scholar
  • Tang W, Yang HT (2019) Optimal operation and bidding strategy of a virtual power plant integrated with energy storage systems and elasticity demand response. IEEE Access 7:79798–79809.Google Scholar
  • Teeraratkul T, O’Neill D, Lall S (2017) Shape-based approach to household electric load curve clustering and prediction. IEEE Trans. Smart Grid 9(5):5196–5206.Google Scholar
  • Vasirani M, Kota R, Cavalcante RL, Ossowski S, Jennings NR (2013) An agent-based approach to virtual power plants of wind power generators and electric vehicles. IEEE Trans. Smart Grid 4(3):1314–1322.Google Scholar
  • Vinsensius A, Wang Y, Chew EP, Lee LH (2020) Dynamic incentive mechanism for delivery slot management in e-commerce attended home delivery. Transportation Sci. 54(3):567–587.LinkGoogle Scholar
  • Wang J, Zhong H, Xia Q, Ma Z, Wang Z, Wu X (2017) Robust bidding strategy for microgrids in joint energy, reserve and regulation markets. Proc. IEEE Power and Energy Soc. General Meeting (IEEE, Piscataway, NJ), 1–5.Google Scholar
  • Wozabal D, Rameseder G (2020) Optimal bidding of a virtual power plant on the Spanish day-ahead and intraday market for electricity. Eur. J. Oper. Res. 280(2):639–655.Google Scholar
  • Yu S, Fang F, Liu Y, Liu J (2019) Uncertainties of virtual power plant: Problems and countermeasures. Appl. Energy 239:454–470.Google Scholar
  • Zamani AG, Zakariazadeh A, Jadid S (2016) Day-ahead resource scheduling of a renewable energy based virtual power plant. Appl. Energy 169:324–340.Google Scholar
  • Zhang Z, Wang J, Ding T, Wang X (2016) A two-layer model for microgrid real-time dispatch based on energy storage system charging/discharging hidden costs. IEEE Trans. Sustainable Energy 8(1):33–42.Google Scholar
  • Zhao Q, Shen Y, Li M (2015) Control and bidding strategy for virtual power plants with renewable generation and inelastic demand in electricity markets. IEEE Trans. Sustainable Energy 7(2):562–575.Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.