In the presence of time-variable energy tariffs, users will try to schedule the usage of their electrical appliances with the goal of minimising their bill. If the variable price component depends on the peak aggregate demand during each given hour, users will be incentivised to redistribute their consumption during the day, thus lowering the overall peak consumption. The process can be automated by means of an Energy Management System that chooses the best schedule while satisfying the user’s constraints on the maximum tolerable delays. In turn, users’ thresholds on delay tolerance may slowly change over time. In fact, users may be willing to modify their threshold to match the threshold of their social group, especially if there is evidence that friends with a more flexible approach have paid a lower bill. We provide an algorithmic framework that models the effect of social interactions in a distributed demand side management system and show that such interactions can increase the flexibility of users’ schedules and lower the peak power, resulting in a smoother usage of energy throughout the day. Additionally, we provide an alternative description of the model by using Markov Chains and study the corresponding convergence times. We conclude that the users reach a steady state after a limited number of interactions.

Evaluating the effects of social interactions on a distributed demand side management system for domestic appliances / Facchini, A.; Rottondi, C.; Verticale, G.. - In: ENERGY EFFICIENCY. - ISSN 1570-646X. - STAMPA. - 10:5(2017), pp. 1175-1188. [10.1007/s12053-017-9510-y]

Evaluating the effects of social interactions on a distributed demand side management system for domestic appliances

Rottondi, C.;
2017

Abstract

In the presence of time-variable energy tariffs, users will try to schedule the usage of their electrical appliances with the goal of minimising their bill. If the variable price component depends on the peak aggregate demand during each given hour, users will be incentivised to redistribute their consumption during the day, thus lowering the overall peak consumption. The process can be automated by means of an Energy Management System that chooses the best schedule while satisfying the user’s constraints on the maximum tolerable delays. In turn, users’ thresholds on delay tolerance may slowly change over time. In fact, users may be willing to modify their threshold to match the threshold of their social group, especially if there is evidence that friends with a more flexible approach have paid a lower bill. We provide an algorithmic framework that models the effect of social interactions in a distributed demand side management system and show that such interactions can increase the flexibility of users’ schedules and lower the peak power, resulting in a smoother usage of energy throughout the day. Additionally, we provide an alternative description of the model by using Markov Chains and study the corresponding convergence times. We conclude that the users reach a steady state after a limited number of interactions.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2723321
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