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CASCADE: an agent based framework for modelling the dynamics of smart electricity systems

CASCADE: an agent based framework for modelling the dynamics of smart electricity systems
CASCADE: an agent based framework for modelling the dynamics of smart electricity systems
The Complex Adaptive Systems, Cognitive Agents and Distributed Energy (CASCADE) project is developing a framework based on Agent Based Modelling (ABM). The CASCADE Framework can be used both to gain policy and industry relevant insights into the smart grid concept itself and as a platform to design and test distributed ICT solutions for smart grid based business entities. ABM is used to capture the behaviors of different social, economic and technical actors, which may be defined at various levels of abstraction. It is applied to understanding their interactions and can be adapted to include learning processes and emergent patterns. CASCADE models ‘prosumer’ agents (i.e., producers and/or consumers of energy) and ‘aggregator’ agents (e.g., traders of energy in both wholesale and retail markets) at various scales, from large generators and Energy Service Companies down to individual people and devices. The CASCADE Framework is formed of three main subdivisions that link models of electricity supply and demand, the electricity market and power flow. It can also model the variability of renewable energy generation caused by the weather, which is an important issue for grid balancing and the profitability of energy suppliers. The development of CASCADE has already yielded some interesting early findings, demonstrating that it is possible for a mediating agent (aggregator) to achieve stable demand flattening across groups of domestic households fitted with smart energy control and communication devices, where direct wholesale price signals had previously been found to produce characteristic complex system instability. In another example, it has demonstrated how large changes in supply mix can be caused even by small changes in demand profile. Ongoing and planned refinements to the Framework will support investigation of demand response at various scales, the integration of the power sector with transport and heat sectors, novel technology adoption and diffusion work, evolution of new smart grid business models, and complex power grid engineering and market interactions.
1521-3250
1-13
Rylatt, M.
5467ad0a-af93-41ea-851e-5b0681cd4b16
Gammon, R.
3ac42311-d390-42a8-be55-c6b02e2009a5
Boait, P.
7eb48760-5439-49ad-b5c1-6dd70f7b2843
Varga, L.
b2110a8f-bd20-4b87-9dd6-c801f5715111
Allen, P.
954f0195-4fae-47d8-8319-ef205e0581c4
Savill, M.
4c68e863-c14a-41dc-85dd-1ee8220fa09d
Snape, R.
226932ed-2a83-4de3-aca2-95eab266954c
Lemon, M.
c88506a4-e940-4734-8168-6dad4bcec442
Ardestani, B.
76660138-4f1c-4c40-81bf-bc00d7be1cee
Pakka, V.
dea98e3f-7924-4e61-82e2-4c441b583756
Fletcher, G.
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Smith, S.
8904521d-3b46-4112-8566-e08de3e011a5
Fan, D.
a80603a2-a7c1-479a-8f3a-6ea27eeafa7b
Strathern, M.
e6dbca82-e716-4f8c-9dd7-8745194e8625
Rylatt, M.
5467ad0a-af93-41ea-851e-5b0681cd4b16
Gammon, R.
3ac42311-d390-42a8-be55-c6b02e2009a5
Boait, P.
7eb48760-5439-49ad-b5c1-6dd70f7b2843
Varga, L.
b2110a8f-bd20-4b87-9dd6-c801f5715111
Allen, P.
954f0195-4fae-47d8-8319-ef205e0581c4
Savill, M.
4c68e863-c14a-41dc-85dd-1ee8220fa09d
Snape, R.
226932ed-2a83-4de3-aca2-95eab266954c
Lemon, M.
c88506a4-e940-4734-8168-6dad4bcec442
Ardestani, B.
76660138-4f1c-4c40-81bf-bc00d7be1cee
Pakka, V.
dea98e3f-7924-4e61-82e2-4c441b583756
Fletcher, G.
2fdf89d0-343a-4a25-9bee-ed215fefdb24
Smith, S.
8904521d-3b46-4112-8566-e08de3e011a5
Fan, D.
a80603a2-a7c1-479a-8f3a-6ea27eeafa7b
Strathern, M.
e6dbca82-e716-4f8c-9dd7-8745194e8625

Rylatt, M., Gammon, R. and Boait, P. et al. (2013) CASCADE: an agent based framework for modelling the dynamics of smart electricity systems. [in special issue: Complexity and the Smart Electricity Grid] Emergence: Complexity & Organisation, 15 (2), 1-13.

Record type: Article

Abstract

The Complex Adaptive Systems, Cognitive Agents and Distributed Energy (CASCADE) project is developing a framework based on Agent Based Modelling (ABM). The CASCADE Framework can be used both to gain policy and industry relevant insights into the smart grid concept itself and as a platform to design and test distributed ICT solutions for smart grid based business entities. ABM is used to capture the behaviors of different social, economic and technical actors, which may be defined at various levels of abstraction. It is applied to understanding their interactions and can be adapted to include learning processes and emergent patterns. CASCADE models ‘prosumer’ agents (i.e., producers and/or consumers of energy) and ‘aggregator’ agents (e.g., traders of energy in both wholesale and retail markets) at various scales, from large generators and Energy Service Companies down to individual people and devices. The CASCADE Framework is formed of three main subdivisions that link models of electricity supply and demand, the electricity market and power flow. It can also model the variability of renewable energy generation caused by the weather, which is an important issue for grid balancing and the profitability of energy suppliers. The development of CASCADE has already yielded some interesting early findings, demonstrating that it is possible for a mediating agent (aggregator) to achieve stable demand flattening across groups of domestic households fitted with smart energy control and communication devices, where direct wholesale price signals had previously been found to produce characteristic complex system instability. In another example, it has demonstrated how large changes in supply mix can be caused even by small changes in demand profile. Ongoing and planned refinements to the Framework will support investigation of demand response at various scales, the integration of the power sector with transport and heat sectors, novel technology adoption and diffusion work, evolution of new smart grid business models, and complex power grid engineering and market interactions.

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More information

Published date: 30 June 2013
Organisations: Faculty of Engineering and the Environment

Identifiers

Local EPrints ID: 368759
URI: http://eprints.soton.ac.uk/id/eprint/368759
ISSN: 1521-3250
PURE UUID: 413f3665-ac32-4ff6-8f24-b24c8d747bdb

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Date deposited: 06 Oct 2014 09:39
Last modified: 15 Jul 2019 21:45

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Contributors

Author: M. Rylatt
Author: R. Gammon
Author: P. Boait
Author: L. Varga
Author: P. Allen
Author: M. Savill
Author: R. Snape
Author: M. Lemon
Author: B. Ardestani
Author: V. Pakka
Author: G. Fletcher
Author: S. Smith
Author: D. Fan
Author: M. Strathern

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