Currently, a group (CIBS and STORM) of colleagues as LondonMet are working on developing a spatial agent-based model of the global energy system using the R statistical package and Repast Symphony (the next 6 months or so I will post more information about it).
However, I have found some sources that can be helpful to people that develop agent-based model of the energy or exlectricity system:
1) Agent-Based Models of Energy Investment Decisions by Tobias Wittmann. Abstract:
At the start of the 21 st century societies face the challenge of securing an
efficient and environmentally sound supply of energy for present and future generations. Sector deregulation, the emergence of novel distributed technologies, firms focusing on these new options and competing in selected markets, and the requirements to reduce energy related greenhouse gas emissions might change the structure of energy systems significantly.
Densely populated urban areas, which allow for the operation of sophisticated energy infrastructures are the most suitable to see essential changes in their energy infrastructure.
This book develops a new model to study the development of urban energy systems. It combines a technical, highly resolved energy system model with an agent-based approach. The technical, highly resolved energy model is used to simulate the operation of technologies. Different agents are developed to capture the investment decisions of actors. Two classes of actors are distinguished: private and commercial actors. The decisions of private actors are modeled using a bounded rational decision model which can be parameterized by socio-demographic surveys. The decisions of commercial actors are approached with a rational choice model, but taking into account different perspectives of firms with regard to future
market developments.
A proof of concept implementation demonstrates the potential of the developed approach. Diffusion curves for conversion technologies and efficiency upgrades in the residential sector were obtained and the overall energy savings were calculated. Further, the impact of firms’ competition on diffusion curves could be estimated and different business models were tested.
2) Achieving A Sustainable Global Energy System: Identifying Possibilities Using Long-Term Energy Scenarios by Asami Miketa (Author), Keywan Riahi (Author), Richard Alexander Roehrl (Author), Leo Schrattenholzer (Editor). Abstract:
Sustainable development and global climate change have figured prominently in scientific analysis and international policymaking since the early 1990s. This book formulates technology strategies that will lead to environmentally sustainable energy systems, based on an analysis of global climate change issues using the concept of sustainable development. The authors focus on environmentally compatible, long-term technology developments within the global energy system, while also considering aspects of economic and social sustainability. The authors analyze a large number of alternative scenarios and illustrate the differences between those that meet the criteria for sustainable development and those that do not. As a result of their analysis, they identify a variety of promising socio-economic and environmental development paths that are consistent with sustainable development. One sustainable-development scenario and its policy implications are then presented in detail from a technology change perspective. The authors propose ambitious targets for technology adoption that are judged to achieve the desired socio-economic and environmental goals. Although the optimal policy mix to pursue these targets is clearly country-specific, the authors suggest that energy-related R&D that leads to technology performance improvements and the promotion of technology adoption in niche markets are the policy options which will yield the most significant long-term benefits. Policymakers, economists and researchers working on sustainability, energy economics, and technology change and innovation will welcome this topical and highly readable book.
3) The AMES Wholesale Power Market Test Bed by Hongyan Li, Junjie Sun, and Leigh Tesfatsion. Details: http://www.econ.iastate.edu/tesfatsi/AMESMarketHome.htm.
4) Electricity Market Complex Adaptive System (EMCAS) by Argonne. Details: http://www.dis.anl.gov/projects/emcas.html.
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