On the planning and analysis of Integrated Community Energy Systems: A review and survey of available tools

摘要

Highly Integrated Community Energy Systems (ICES) greatly but not solely dependent on combined heat and power (CHP) sources are a viable approach for dealing effectively with the new set of global threats which Mankind is facing, such as Climate Change, Global Warming and Extreme Poverty. ICES are capable of delivering sustainable electricity, heat and cold to small communities and of working as grid-connected or islanded microgrids, adding technical, economical, environmental and social benefits to populations. The impacts of introducing ICES in current distribution networks can be analyzed at different scales due to the wide range of influence exerted not only at the local but also at regional and global levels. For these reasons, there is increased need for appropriate modeling of ICES for the vital purposes of planning and analysis of these systems. An overview on the available bottom-up tools for the optimization planning and analysis of ICES is done in this paper. The survey shows that DER-CAM can be considered an appropriate tool for the purpose of ICES design modeling due to the robust and flexible three-level optimization algorithm, hourly time step and other scale considerations but particularly due to the several successful applications with modeling microgrid systems. Additionally there is research experience on expanding the objective function for environmental concerns and also with EV battery storage interactions. Finally, CAMS DER-CAM's base language, is a widely known package for allowing changes to be made in model specifications simply and safely. In that sense, there is potential in exploring such tool for the design of ICES. Furthermore, it is found that MARKAL/TIMES, also a GAMS/CPLEX based tool, has scale flexibility which enables it for analyzing the long-term deployment of ICES in time. There is opportunity in this field for further work exploring the sustainability-sound modeling for optimal design of ICES and deployment scenario options evaluation, through long-term time horizons consideration.