[1] S. Rafique and Z. Jianhua, “Energy management system, generation and demand predictors: A review”, IET Gener. Transm. Distrib., vol. 12, pp. 519-530, 2018.
[2] Status of Power System Transformation: Power system flexibility–Analysis-IEA, 2019.
[3] J. Aghaei et al., “Exploring the reliability effects on the short term AC security-constrained unit commitment: A stochastic evaluation”, Energy, vol. 114, pp. 1016-32, 2016.
[4] M. Shamshirband, J. Salehi and F. Gazijahani, “Look-ahead risk-averse power scheduling of heterogeneous electric vehicles aggregations enabling V2G and G2V systems based on information gap decision theory”, Electr. Power Syst. Res., vol. 173, pp. 56-70, 2019.
[5] cVPP - Community-based Virtual Power Plant | Interreg NWE., 2020.
[6] Fenix, http://fenix.iee.fraunhofer.de/html/what.htm.
[7] S. Nosratabadi, R. Hooshmand and E. Gholipour, “Stochastic profit-based scheduling of industrial virtual power plant using the best demand response strategy”, Appl. Energy, vol. 164, pp. 590-606, 2016.
[8] L. Lin et al., “Deep reinforcement learning for economic dispatch of virtual power plant in internet of energy”, IEEE Internet of Things J., vol. 7, pp. 6288-301, 2020.
[9] M. Pasetti, S. Rinaldi and D. Manerba, “A virtual power plant architecture for the demand-side management of smart prosumers”, Appl. Sci., vol. 8, 2018.
[10] A. Zamani, A. Zakariazadeh and S. Jadid, “Day-ahead resource scheduling of a renewable energy based virtual power plant”, Appl. Energy, vol. 169, pp. 324-40, 2016.
[11] M. Alipour et al., “Stochastic scheduling of aggregators of plug-in electric vehicles for participation in energy and ancillary service markets”, Energy, vol. 118, pp. 1168–1179, 2017.
[12] A. Shayegan-Rad, A. Badri and A. Zangeneh, “Day-ahead scheduling of virtual power plant in joint energy and regulation reserve markets under uncertainties”, Energy, vol. 121, pp. 114-125, 2017.
[13] J. Qiu, K. Meng, Y. Zheng and Z. Dong, “Optimal scheduling of distributed energy resources as a virtual power plant in a transactive energy framework”, IET Gener. Transm. Distrib., vol. 11, pp. 3417-27, 2017.
[14] H. Howlader, H. Matayoshi, and T. Senjyu, “Distributed generation integrated with thermal unit commitment considering demand response for energy storage optimization of smart grid”, Renew. Energy, vol. 99, pp. 107-117, 2016.
[15] A. Abdolahi, F. Gazijahani, A. Alizadeh, and N. Kalantari, “Chance-constrained CAES and DRP scheduling to maximize wind power harvesting in congested transmission systems considering operational flexibility”, Sustain. Cities Soc., vol. 51, pp. 101792, 2019.
[16] Z. Wu, P. Zeng, X. Zhang, and Q. Zhou, “A Solution to the chance-constrained two-stage stochastic program for unit commitment with wind energy integration”, IEEE Trans. Power Syst., vol. 31, pp. 4185-96, 2016.
[17] S. Hadayeghparast, A. Farsangi, and H. Shayanfar, “Day-ahead stochastic multi-objective economic/emission operational scheduling of a large scale virtual power plant”, Energy, vol. 172, pp. 630-646, 2019.
[18] Y. Jiang et al., “Coordinated operation of gas-electricity integrated distribution system with multi-CCHP and distributed renewable energy sources”, Appl. Energy, vol. 211, pp. 237-248, 2018.
[19] Z. Liang, Q. Alsafasfeh, T. Jin, H. Pourbabak, and W. Su, “Risk-constrained optimal energy management for virtual power plants considering correlated demand response”, IEEE Trans. Smart Grid, vol. 10, pp. 1577-87, 2019.
[20] M. Nazari and M. Ardehali, “Profit-based unit commitment of integrated CHP-thermal-heat only units in energy and spinning reserve markets with considerations for environmental CO2 emission cost and valve-point effects”, Energy, vol. 133, pp. 621-35, 2017.
[21] M. Behnamfar, H. Barati, and M. Karami, “Stochastic Short-Term Hydro-Thermal Scheduling Based on Mixed Integer Programming with Volatile Wind Power Generation”, J. Oper. Autom. Power Eng., vol. 8, pp. 195-208, 2020.
[22] J. Zhang et al. “A hybrid particle swarm optimization with small population size to solve the optimal short-term hydro-thermal unit commitment problem”, Energy, vol. 109, pp. 765-80, 2016.
[23] D. Lee et al., “Security-Constrained Unit Commitment Considering Demand Response Resource as Virtual Power Plant”, IFAC-Papers OnLine, vol. 49, pp. 290-295, 2016.
[24] A. Lorca and X. Sun, “Multistage Robust Unit Commitment with Dynamic Uncertainty Sets and Energy Storage”, IEEE Trans. Power Syst., vol. 32, pp. 1678-1688, 2017.
[25] L. Ju et al., “A CVaR-robust-based multi-objective optimization model and three-stage solution algorithm for a virtual power plant considering uncertainties and carbon emission allowances”, Int. J. Electr. Power Energy Syst., vol. 107, pp. 628-643, 2019.
[26] L. Ju et al., “A multi-objective robust scheduling model and solution algorithm for a novel virtual power plant connected with power-to-gas and gas storage tank considering uncertainty and demand response”, Appl. Energy, vol. 250, pp. 1336-55, 2019.
[27] A. Zangeneh, A. Shayegan-Rad, and F. Nazari, “Multi-leader–follower game theory for modelling interaction between virtual power plants and distribution company”, IET Gener. Transm. Distrib., vol. 12, pp. 5747-52, 2018.
[28] X. Kong et al., “Bi-level multi-time scale scheduling method based on bidding for multi-operator virtual power plant”, Appl. Energy, vol. 249, pp. 178-189, 2019.
[29] E. Kardakos, C. Simoglou and A. Bakirtzis, “Optimal Offering Strategy of a Virtual Power Plant: A Stochastic Bi-Level Approach”, IEEE Trans. Smart Grid, vol. 7, pp. 794-806, 2016.
[30] M. Charwand et al., “Robust hydrothermal scheduling under load uncertainty using information gap decision theory”, Int. Trans. Electr. Energy Syst., vol. 26, pp. 464-485, 2016.
[31] M. Nazari-Heris, P. Aliasghari, B. Mohammadi-Ivatloo, and M. Abapour, “Optimal robust scheduling of renewable energy-based smart homes using information-gap decision theory (Igdt)”, Robust Optim. Plan. Oper. Electr. Energy Syst., pp. 95-106, 2019.
[32] M. Vahid-Ghavidel, N. Mahmoudi, and B. Mohammadi-Ivatloo, “Self-scheduling of demand response aggregators in short-term markets based on information gap decision theory”, IEEE Trans. Smart Grid, vol. 10, pp. 2115-26, 2019.
[33] A. Najafi-Ghalelou, S. Nojavan, and K. Zare, “Robust thermal and electrical management of smart home using information gap decision theory”, Appl. Therm. Eng., vol. 132, pp. 221-232, 2018.
[34] A. Soroudi, A. Rabiee, and A. Keane, “Information gap decision theory approach to deal with wind power uncertainty in unit commitment”, Electr. Power Syst. Res., vol. 145, pp. 137-148, 2017.
[35] F. 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., vol. 13, pp. 1767-75, 2019.
[36] F. Gazijahani and J. Salehi, “IGDT-Based Complementarity Approach for Dealing with Strategic Decision Making of Price-Maker VPP Considering Demand Flexibility”, IEEE Trans. Ind. Informatics, vol. 16, pp. 2212-20, 2020.
[37] R. Alasseri, A. Tripathi, T. Joji Rao, and K. Sreekanth, “A review on implementation strategies for demand side management (DSM) in Kuwait through incentive-based demand response programs”, Renew. Sustain. Energy Rev., vol. 77, pp. 617-35, 2017.
[38] S. Ghaderi, H. Shayeghi, Y. Hashemi, “Impact of Demand Response Technique on Hybrid Transmission expansion planning and Reactive Power planning,” J. Oper. Autom. Power Eng., In press, 2019.
[39] H. Haider, O. See, and W. Elmenreich, “A review of residential demand response of smart grid”, Renew. Sustain. Energy Rev., vol. 59, pp. 166-78, 2016.
[40] S. Zhou et al., “Demand response program in Singapore’s wholesale electricity market”, Electr. Power Syst. Res., vol. 142, pp. 279-89, 2017.
[41] A. Jordehi, “Optimisation of demand response in electric power systems, a review”, Renew. Sustain. Energy Rev., vol. 103, pp. 308-19, 2019.
[42] J. Wang et al., “Review and prospect of integrated demand response in the multi-energy system”, Appl. Energy, vol. 202, pp. 772-82, 2017.
[43] E. Castillo et al., “Optimality and Duality in Non-linear Programming”, Build. Solving Math. Program. Models Eng. Sci., 2001.
[44] A. Conejo et al., “Other Decomposition Techniques -Decomposition techniques in mathematical programming: Engineering and science applications”, Decompos. Tech. Math. Program. Eng. Sci. Appl., 2006.
[45] Y. Ben-Haim, “Robustness and Opportuneness”, Info Gap Decision Theory, 2006.
[46] M. Rostami and S. Lotfifard, “optimal remedial actions in power systems considering wind farm grid codes and UPFC”, IEEE Trans. Ind. Inf., vol. 16, pp. 7264-74, 2020.
[47] J. Aghaei et al., “Optimal robust unit commitment of CHP plants in electricity markets using information gap decision theory”, IEEE Trans. Smart Grid, vol. 8, pp. 2296-04, 2017.
[48] L. Ju et al., “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, vol. 171, pp. 184-99, 2016.
[49] C. Ordoudis, P. Pinson, J. González, and M. Zugno, “An updated version of the IEEE RTS 24-Bus system for electricity market and power system operation studies”, Tech. Univ. Denmark, pp. 1-6, 2016.
[50] A. Conejo, M. Carrión, and J. Morales, “International series in operations research and management science, Electricity Markets”, Decis. Making Uncertainty Electr. Markets, Springer, 2010.
[51] “GAMS - Cutting Edge Modeling.” https://www.gams.com/
[52] “Market data | Nord Pool.” https://www.nordpoolgroup.com/Market-data1/GB/Auction-prices/UK/Hourly/?view=table.
[53] A. Soroudi, A. Rabiee, and A. Keane, “Information gap decision theory approach to deal with wind power uncertainty in unit commitment”, Electr. Power Syst. Res., vol. 145, pp. 137-148, 2017.