Free Access
RAIRO-Oper. Res.
Volume 54, Number 4, July-August 2020
Page(s) 993 - 1012
Published online 28 April 2020
  • A. Abdulkarim, S.M. Abdelkader and D.J. Morrow, Statistical analyses of wind and solar energy resources for the development of hybrid microgrid. Springer (2015) 9–14. [Google Scholar]
  • M. Aman, G. Jasmon, H. Mokhlis and A. Bakar, Optimal placement and sizing of a DG based on a new power stability index and line losses. Int. J. Electr. Power Energy Syst. 43 (2012) 1296–1304. [CrossRef] [Google Scholar]
  • G.E. Asimakopoulou, A.L. Dimeas and N.D. Hatziargyriou, Leader-follower strategies for energy management of multi-microgrids. IEEE Trans. Smart Grid 4 (2013) 1909–1916. [Google Scholar]
  • S. Bahramara and H. Golpra, Robust optimization of micro-grids operation problem in the presence of electric vehicles. Sustainable Cities Soc. 37 (2017) 388–395. [CrossRef] [Google Scholar]
  • A. Ben-Tal and A. Nemirovski, Robust solutions of uncertain linear programs. Oper. Res. Lett. 25 (1999) 1–13. [CrossRef] [Google Scholar]
  • D. Bertsimas and M. Sim, The price of robustness. Oper. Res. 52 (2004) 35–53. [Google Scholar]
  • D. Bertsimas and D.B. Brown, Constructing uncertainty sets for robust linear optimization. Oper. Res. 57 (2009) 1483–1495. [Google Scholar]
  • E. Carpaneto and G. Chicco, Probability distributions of the aggregated residential load. In: 2006 International Conference on Probabilistic Methods Applied to Power Systems. IEEE (2006) 1–6. [Google Scholar]
  • E. Carpaneto and G.J.I.G. Chicco, Probabilistic characterisation of the aggregated residential load patterns. IET Gener. Transm. Distrib. 2 (2008) 373–382. [CrossRef] [Google Scholar]
  • Z. Chen, L. Wu and Y. Fu, Real-time price-based demand response management for residential appliances via stochastic optimization and robust optimization. IEEE Trans. Smart Grid 3 (2012) 1822–1831. [Google Scholar]
  • S. Dempe, Foundations of Bilevel Programming. Springer Science & Business Media, Berlin (2002). [Google Scholar]
  • X. Fang, F. Li, Y. Wei and H. Cui, Strategic scheduling of energy storage for load serving entities in locational marginal pricing market. IET Gener. Transm. Distrib. 10 (2016) 1258–1267. [CrossRef] [Google Scholar]
  • F.S. Gazijahani and J. Salehi, Robust design of microgrids with reconfigurable topology under severe uncertainty. IEEE Trans. Sustainable Energy 9 (2018) 559–569. [CrossRef] [Google Scholar]
  • F.S. Gazijahani and J. Salehi, Reliability constrained two-stage optimization of multiple renewable-based microgrids incorporating critical energy peak pricing demand response program using robust optimization approach. Energy 161 (2018) 999–1015. [CrossRef] [Google Scholar]
  • F.S. Gazijahani and J. Salehi, Game theory based profit maximization model for microgrid aggregators with presence of EDRP using information gap decision theory. IEEE Syst. J. 1–9 (2018). [Google Scholar]
  • F.S. Gazijahani, S.N. Ravadanegh and J. Salehi, Stochastic multi-objective model for optimal energy exchange optimization of networked microgrids with presence of renewable generation under risk-based strategies. ISA Trans. 73 (2018) 100–111. [Google Scholar]
  • H. Golpra, Supply chain network design optimization with risk-averse retailer. Int. J. Inf. Syst. Supply Chain Manage. 10 (2017) 16–28. [CrossRef] [Google Scholar]
  • H. Golpra, Robust bi-level optimization for an opportunistic supply chain network design problem in an uncertain and risky environment. Oper. Res. Decis. 27 (2017). [Google Scholar]
  • H. Golpra, A novel multiple attribute decision making approach based on interval data using U2P-Miner algorithm. Data Knowl. Eng. 115 (2018) 116–128. [Google Scholar]
  • H. Golpra and S.A.R. Khan, A multi-objective risk-based robust optimization approach to energy management in smart residential buildings under combined demand and supply uncertainty. Energy 170 (2019) 1113–1129. [CrossRef] [Google Scholar]
  • H. Golpra, E. Najafi, M. Zandieh and S. Sadi-Nezhad, Robust bi-level optimization for green opportunistic supply chain network design problem against uncertainty and environmental risk. Comput. Ind. Eng. 107 (2017) 301–312. [Google Scholar]
  • H. Golpra, S.A.R. Khan and Y. Zhang, Robust smart energy efficient production planning for a general job-shop manufacturing system under combined demand and supply uncertainty in the presence of grid-connected microgrid. J. Cleaner Prod. 202 (2018) 649–665. [CrossRef] [Google Scholar]
  • J. Lee, J. Guo, J.K. Choi and M. Zukerman, Distributed energy trading in microgrids: A game-theoretic model and its equilibrium analysis. IEEE Trans. Ind. Electron. 62 (2015) 3524–3533. [Google Scholar]
  • X. Lu, K. Zhou and S. Yang, Multi-objective optimal dispatch of microgrid containing electric vehicles. J. Cleaner Prod. 165 (2017) 1572–1581. [CrossRef] [Google Scholar]
  • L. Ma, N. Liu, J. Zhang, W. Tushar and C. Yuen, Energy management for joint operation of CHP and PV prosumers inside a grid-connected microgrid: a game theoretic approach. IEEE Trans. Ind. Inf. 12 (2016) 1930–1942. [CrossRef] [Google Scholar]
  • A. Mehdizadeh, N. Taghizadegan and J. Salehi, Risk-based energy management of renewable-based microgrid using information gap decision theory in the presence of peak load management. Appl. Energy 211 (2018) 617–630. [Google Scholar]
  • A. Mondal, S. Misra and M.S. Obaidat, Distributed home energy management system with storage in smart grid using game theory. IEEE Syst. J. 11 (2017) 1857–1866. [Google Scholar]
  • J.M. Morales, P. Pinson and H. Madsen, A transmission-cost-based model to estimate the amount of market-integrable wind resources. IEEE Trans. Power Syst. 27 (2012) 1060–1069. [Google Scholar]
  • N. Nikmehr, S. Najafi-Ravadanegh and A. Khodaei, Probabilistic optimal scheduling of networked microgrids considering time-based demand response programs under uncertainty. Appl. Energy 198 (2017) 267–279. [Google Scholar]
  • S.M. Nosratabadi, R.-A. Hooshmand and E. Gholipour, A comprehensive review on microgrid and virtual power plant concepts employed for distributed energy resources scheduling in power systems. Renew. Sustainable Energy Rev. 67 (2017) 341–363. [CrossRef] [Google Scholar]
  • M. Patriksson and L. Wynter, Stochastic mathematical programs with equilibrium constraints. Oper. Res. Lett. 25 (1999) 159–167. [CrossRef] [MathSciNet] [Google Scholar]
  • N. Rezaei, A. Ahmadi, A. Khazali and J.M. Guerrero, Energy and frequency hierarchical management system using information gap decision theory for islanded microgrids. IEEE Trans. Ind. Electron. 65 (2018) 7921–7932. [Google Scholar]
  • A. Thiele, A robust optimization approach to supply chains and revenue management. Ph.D dissertion Massachusetts Institute of Technology (2004). [Google Scholar]
  • E.C. Umeozor and M. Trifkovic, Operational scheduling of microgrids via parametric programming. Appl. Energy 180 (2016) 672–681. [Google Scholar]
  • Z. Wang, B. Chen, J. Wang, M.M. Begovic and C. Chen, Coordinated energy management of networked microgrids in distribution systems. IEEE Trans. Smart Grid 6 (2015) 45–53. [Google Scholar]
  • L. Wang, Q. Li, R. Ding, M. Sun and G. Wang, Integrated scheduling of energy supply and demand in microgrids under uncertainty: a robust multi-objective optimization approach. Energy 130 (2017) 1–14. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.