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Design method for a local energy cooperative network using distributed energy technologies

Abstract We propose a method for designing a local energy cooperative network (EneCoN) that uses new technologies for heating and power in a combined way. The method comprises six steps, including the generation of different technology options, energy balance modeling of the demand and supply, and multiobjective evaluation and optimization. As a decision-support tool, ternary diagrams are applied to analyze the effect of the mix of energy consumers in an EneCoN (i.e., residents, offices, or hospitals) on the objective function. The overall outcome of applying the method is a suggestion for the technology mix in the EneCoN that minimizes design objectives such as cost and environmental impacts with consideration of the mix of energy consumers as a design parameter. A case study was conducted on the installation of photovoltaic power generators, solar heat collectors, and fuel cells as new energy technologies in the target cities Tokyo, Sapporo, and Naha. Differences in the climate and energy demand profile were well reflected in the calculation, and different suggestions for the technology mix were obtained. The method also allowed good visualization of various complex design options (e.g., consumers, technology types, and the degree of combination) and can serve as a solid basis for designing energy systems.
- International Institute of Minnesota United States
- University of Tokyo Japan
- International Institute of Minnesota United States
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).20 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
