Using collective intelligence to enhance demand flexibility and climate resilience in urban areas
Nik, Vahid M. LU
and Moazami, Amin
(2021)
In Applied Energy
281.
- 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... (More)
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.
(Less)
- author
- Nik, Vahid M.
LU
and Moazami, Amin
- organization
- publishing date
- 2021
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Climate flexibility, Climate resilience, Collective intelligence, Demand flexibility, Demand side management, Urban energy system
- in
- Applied Energy
- volume
- 281
- article number
- 116106
- publisher
- Elsevier
- external identifiers
-
- scopus:85094593134
- ISSN
- 0306-2619
- DOI
- 10.1016/j.apenergy.2020.116106
- project
- Collective Intelligence for Energy Flexibility
- Flexible energy system integration using concept development, demonstration and replication
- language
- English
- LU publication?
- yes
- id
- be0a68e5-d1bc-4679-8e59-0f7f879aff5f
- date added to LUP
- 2020-11-13 07:23:08
- date last changed
- 2023-02-01 20:46:39
@article{be0a68e5-d1bc-4679-8e59-0f7f879aff5f,
abstract = {{<p>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.</p>}},
author = {{Nik, Vahid M. and Moazami, Amin}},
issn = {{0306-2619}},
keywords = {{Climate flexibility; Climate resilience; Collective intelligence; Demand flexibility; Demand side management; Urban energy system}},
language = {{eng}},
publisher = {{Elsevier}},
series = {{Applied Energy}},
title = {{Using collective intelligence to enhance demand flexibility and climate resilience in urban areas}},
url = {{http://dx.doi.org/10.1016/j.apenergy.2020.116106}},
doi = {{10.1016/j.apenergy.2020.116106}},
volume = {{281}},
year = {{2021}},
}