Optimal policy for under frequency load shedding based on heterogeneous Markovian opinion dynamics model

Xie, 2020, Consensus weighting of a multi-agent system for load shedding, Int. J. Electr. Power Energy Syst., 117, 10.1016/j.ijepes.2019.105615 Bolzern, 2018, Opinion dynamics in social networks with heterogeneous Markovian agents, 6180 Wang, 2016, Intelligent under frequency and under voltage load shedding method based on the active participation of smart appliances, IEEE Trans. Smart Grid, 8, 353, 10.1109/TSG.2016.2582902 Abdelwahid, 2014, Hardware implementation of an automatic adaptive centralized underfrequency load shedding scheme, IEEE Trans. Power Delivery, 29, 2664, 10.1109/TPWRD.2014.2331495 Rudez, 2015, WAMS-based underfrequency load shedding with short-term frequency prediction, IEEE Trans. Power Delivery, 31, 1912, 10.1109/TPWRD.2015.2503734 Potel, 2019, A real-time adjustment of conventional under-frequency load shedding thresholds, IEEE Trans. Power Delivery, 34, 2272, 10.1109/TPWRD.2019.2900594 Xu, 2011, Stable multi-agent-based load shedding algorithm for power systems, IEEE Trans. Power Syst., 26, 2006, 10.1109/TPWRS.2011.2120631 Yang, 2015, Minimum-time consensus-based approach for power system applications, IEEE Trans. Ind. Electron., 63, 1318, 10.1109/TIE.2015.2504050 Xie, 2017, 1 Abelson, R. P. (1964). Mathematical models of the distribution of attitudes under controversy. Contributions to mathematical psychology. Acemoğlu, 2013, Opinion fluctuations and disagreement in social networks, Mathematics of Operations Research, 38, 1, 10.1287/moor.1120.0570 Asavathiratham, 2001, The influence model, IEEE Control Syst. Mag., 21, 52, 10.1109/37.969135 Banisch, 2012, Agent based models and opinion dynamics as Markov chains, Social Networks, 34, 549, 10.1016/j.socnet.2012.06.001 Bolzern, 2019, Opinion influence and evolution in social networks: A Markovian agents model, Automatica, 100, 219, 10.1016/j.automatica.2018.11.023 Bond, 2012, A 61-million-person experiment in social influence and political mobilization, Nature, 489, 295, 10.1038/nature11421 Bowden, 2008, The impact of interaction and social learning on aggregate expectations, Comput. Econ., 31, 289, 10.1007/s10614-007-9118-y Puterman, 1990, Markov decision processes, 2, 331