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BRAIN-Energy (Bounded Rationality Agents Investments model) is an agent-based model of electricity generation and investment.
In BRAIN-Energy agents are heterogeneous, have heterogeneous characteristics and strategies. The aim of BRAIN-Energy is to study the aggregate effects of the investment decisions of the heterogeneous agents on the long-run evolution of the electricity sector to 2050, and to capture the emergent techno-economic trends in low-carbon investments arising from the heterogeneous micro-economic investment strategies of the agents. The main agents in BRAIN-Energy are investors in the electricity market (incumbent utilities, new-entrants, local suppliers and households), and policy agents (national government agent, regulator agent and local government agents). Agents in BRAIN-Energy have bounded rationality. They have limited foresight of the future, and heir investment choices are based on their own heterogeneous expectations of electricity demand, fuel and technology costs. Moreover, investments are affected by learning from own past investments (self-learning) and imitation of other investors’ successful strategies. The investment choices of the investor agents co-evolve with the surrounding policy environment and governance structure.
Each year, investor agents decommission unprofitable power plants, take operational decisions about electricity production from their existing stock of assets, and subsequently reassess the profitability of prior investments and take decisions about building new power stations. Investment decisions are taken by each investor independently, but at the same time each of them is confronted with the outcomes of the investment decisions of the others.
BRAIN-Energy’s original version was implemented in Netlogo, and was calibrated to the UK, German and Italian electricity sectors. The newest version is only calibrated to the UK, and gives a stylised representation of the UK electricity sector in terms of generation technologies to reach UK’s net-zero target at 2050, installed capacity, agents (investor agents and policy agents on a national and local level), policies in the energy sector and climate change targets. It is implemented in Python using an object-oriented programming framework, it is calibrated to 2012 as a base year, and it proceeds to 2050. Eight time-slices per year are adopted to represent the temporal variations of electricity supply and demand of the UK-wide and local electricity systems.
Model summary
| Type: | Agent-based model |
| Purpose: | Analysing the strategic investment decisions in power generation assets of different actors with bounded-rationality, and the effects of their myopic and path-dependent choices and their interactions on the long-run evolution of the electricity sector to 2050 |
| Spatial scale: | UK, Germany and Italy |
| Temporal scale: | 2012 to 2050 |
Documentation
Publications
- Barazza, E. and Strachan, N. (2021) "The key role of historic path-dependency and competitor imitation on the electricity sector low-carbon transition", Energy Strategy Reviews, 33. doi:10.1016/j.esr.2020.100588
- Barazza, E. and Strachan, N. (2020) “The impact of heterogeneous market players with bounded-rationality on the electricity sector low-carbon transition”. Energy Policy, 138, doi:10.1016/j.enpol.2020.111274
- Barazza, E. and Strachan, N. (2020) “The co-evolution of climate policy and investments in electricity markets: Simulating agent dynamics in UK, German and Italian electricity sectors”. Energy Research and Social Science, 65. doi:10.1016/j.erss.2020.101458