Using collective intelligence to enhance demand flexibility and climate resilience in urban areas. (1st January 2021)

• Record Type: Journal Article
• Authors: Nik, Vahid M.; Moazami, Amin
• Is Part Of: Applied energy. Volume 281(2021)
• Publication Date: 2021-01-01
• Subjects: Collective intelligence -- Demand flexibility -- Climate flexibility -- Climate resilience -- Demand side management -- Urban energy system; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2020.116106
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Collective intelligence is applied to manage the demand response among buildings. A simple set of rules of engagement and two response timescales are set. A finer timescale helps the system to become more effective and agile in absorbing shocks. CI improves the autonomy in absorbing shocks without the need for upgrading the central control. CI increases the climate flexibility on the demand side, promoting climate resilience. Abstract: Collective intelligence (CI) is a form of distributed intelligence that emerges in collaborative problem solving and decision making. This work investigates the potentials of CI in demand side management (DSM) in urban areas. CI is used to control the energy performance of representative groups of buildings in Stockholm, aiming to increase the demand flexibility and climate resilience in the urban scale. CI-DSM is developed based on a simple communication strategy among buildings, using forward (1) and backward (0) signals, corresponding to applying and disapplying the adaptation measure, which is extending the indoor temperature range. A simple platform and algorithm are developed for modelling CI-DSM, considering two timescales of 15 min and 60 min. Three climate scenarios are used to represent typical, extreme cold and extreme warm years in Stockholm. Several indicators are used to assess the performance of CI-DSM, including Demand Flexibility Factor ( DFF ) and Agility Factor ( AF ), which are defined explicitly for this work. According to the results, CI increases the autonomy and agility of the system in responding to climate shocks without the need for computationally extensive central decision making systems. CI helps to gradually and effectively decrease the energy demand and absorb the shock during extreme climate events. Having a finer control timescale increases the flexibility and agility on the demand side, resulting in a faster adaptation to climate variations, shorter engagement of buildings, faster return to normal conditions and consequently a higher climate resilience.