Public policy modeling of distributed energy technologies: strategies, attributes, and challenges

The systems which provide active and passive energy-services are undergoing rapid institutional, commercial, and technical change. As part of this transformation, distributed energy technologies are expected to play a greater role. In addition, governments and local authorities are seeking to encourage selected distributed technologies, including wind power and cogeneration, for reasons of public interest. Even so, most energy sector policy support models have difficulty realizing distributed technologies, particularly where complex component/system interactions arise. High-resolution modeling addresses these shortcomings through increased topological resolution, greater temporal disaggregation, extended model scope, and support for context-dependent component performance. Examples using the deeco (dynamic energy, emissions, and cost optimization) energy system modeling environment are given. Multi-agent simulation and high-resolution modeling have similar underlying architectures and can be combined to yield entity-oriented modeling. This new technique additionally supports decentralized decision-making, automatically captures interacting commercial and technical dynamics, and may be used to investigate structural evolution. A summary of national energy policy modeling strategies and a roadmap are provided.