TY - GEN
T1 - Consumer Participation in Demand Response Programs: Development of a Consumat-Based Toy Model
AU - Schwarzer, J.
AU - Engel, D.
N1 - Conference code: 276379
Export Date: 14 December 2023
Correspondence Address: Schwarzer, J.; Centre for Secure Energy Informatics, Urstein Sued 1, Austria; email: [email protected]
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Cities Soc., 31, pp. 173-182; Bonabeau, E., (2002) Agent-Based Modeling: Methods and Techniques for Simulating Human Systems, 99, pp. 7280-7287. , vol., pp; Le Page, C., Bazile, D., Becu, N., Bommel, P., Agent-based modelling and simulation applied to environmental management (2013) Edmonds, B., Meyer, R. (Eds.) Simulating Social Complexity. Springer; Schwarzer, J., Engel, D., Agent-based modeling of consumer participation in demand response programs with the consumat framework (2020) Abstracts from the 9Th DACH+ Conference on Energy Informatics, Vol. 3, No. 27, Pp. 13–15; Jager, W., Janssen, M.A., Vlek, C.A.J., (1999) Consumats in a Commons Dilemma: Testing the Behavioural Rules of Simulated Consumers; Schaat, S., Jager, W., Dickert, S., Psychologically plausible models in agent-based simulations of sustainable behavior (2017) Agent-Based Modeling of Sustainable Behaviors, pp. 1-25. , Alonso-Betanzos, A., Sánchez-Maroño, N., Fontenla-Romero, O., Polhill, J.G., Craig, T., Bajo, J., Corchado, J.M. (eds.), pp., Springer International Publishing, Cham; Moglia, M., Podkalicka, A., McGregor, J., An agent-based model of residential energy efficiency adoption (2018) J. Artif. Soc. Soc. Simul., 21 (3), p. 26; Janssen, M.A., Jager, W., Stimulating diffusion of green products (2002) —co-evolution between Firms and Consumers. J. 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(ICIT), pp. 1311-1316. , 2015; Li, N., Chen, L., Low, S.H., Optimal demand response based on utility maximization in power networks (2011) IEEE Power Energy Society General Meeting; Seetharam, D., Bapat, T., Sengupta, N., Ghai, S.K., Shrinivasan, Y.B., Arya, V., (2011) User-Sensitive Scheduling of Home Appliances, p. 43. , p; Adika, C.W.L., Autonomous appliance scheduling for household energy management (2014) IEEE Trans. Smart Grid, 5 (2), pp. 673-682; Barbato, A., Capone, A., Rodolfi, M., Tagliaferri, D., Forecasting the usage of household appliances through power meter sensors for demand management in the smart grid (2011) IEEE International Conference on Smart Grid Communication, pp. 404-409. , pp., 2011; Bandini, S., Manzoni, S., Vizzari, G., Agent based modeling and simulation: An informatics perspective (2009) J. Artif. Soc. Soc. Simul., 12 (4), p. 4; Janssen, M., Ostrom, E., Empirically based, agent-based models (2006) Ecol. 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Artif. Soc. Soc. Simul., 11 (3), p. 7
PY - 2022
Y1 - 2022
N2 - Modeling of the smart grid architecture and its subsystems is a basic requirement for the success of these new technologies to address climate change effects. For a comprehensive research especially on effects of demand response systems, the integration of consumers’ decisions and interactions is essential. To model consumer participation in demand response programs this paper introduces an agent-based approach using the Consumat framework. The implementation in NetLogo provides high scalability and flexibility concerning input parameters and can easily interact with other simulation frameworks. It also forms a possible basis for an overall demand response consumer model. As a so-called toy model, simple correlations in this socio-technical scenario can already be explored. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
AB - Modeling of the smart grid architecture and its subsystems is a basic requirement for the success of these new technologies to address climate change effects. For a comprehensive research especially on effects of demand response systems, the integration of consumers’ decisions and interactions is essential. To model consumer participation in demand response programs this paper introduces an agent-based approach using the Consumat framework. The implementation in NetLogo provides high scalability and flexibility concerning input parameters and can easily interact with other simulation frameworks. It also forms a possible basis for an overall demand response consumer model. As a so-called toy model, simple correlations in this socio-technical scenario can already be explored. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
KW - Agent
KW - Consumat
KW - Demand response
KW - Toy model
U2 - 10.1007/978-3-030-92843-8_24
DO - 10.1007/978-3-030-92843-8_24
M3 - Conference contribution
SN - 978-3-030-92842-1
T3 - Springer Proceedings in Complexity
SP - 315
EP - 327
BT - Advances in Social Simulation
PB - Springer International Publishing
T2 - 16th Social Simulation Conference, SSC 2021
Y2 - 20 September 2021 through 24 September 2021
ER -
SDG 7 Affordable and Clean Energy
SDG 13 Climate Action