Subsidy and social systems should be designed to effectively promote, for example, the introduction of a mechanical system. For an effective subsidy distribution, developing evidence-based decision-making methodologies is essential. We developed a tool that minimizes an objective function includes economic costs, environmental cost, and disaster risk to determine the amount of energy distributed by the equipment when installing in hospitals against disasters. Open data available on the Internet are used as simulation values herein. This simulation model optimizes six design values using a genetic algorithm based on the values (business site area, demand for heat and electricity, and maximum allowable loss) input by customers for their construction and visualizes the total cost. The tool allows to quantitatively assess the changes in the initial and running costs owing to equipment installation and the avoidable losses related to CO 2 emission reductions and disaster risks. In addition, the net present value (NPV) can be calculated using the obtained values for initial and running costs, thereby allowing to clearly estimate the return on investments after equipment installation. The opportunity profit can be calculated based on the difference between the NPV after 15 years assuming no risk and with disaster risks. A method to set this opportunity profit as a calculated subsidy amount is proposed herein. Decision support can be provided via these simulations to the customers and government subsidy system design engineers.
In hospitals, the energy supply is the key to ensuring modern medical care even during power outages due to a disaster. This study qualitatively examined whether the supply-demand balance can be stabilized by the private generator prepared by the hospital building during stand-alone operations under disaster conditions. In the nanogrid of the hospital building, the power quality was examined based on the AC frequency, which characterizes the supply-demand balance. Gas engine generators, emergency diesel generators, photovoltaic panels, and storage batteries were presumed to be the private generators in the hospital building. The output reference values for the emergency diesel and gas engine generators were set using droop control, and the C/D controller enabled synchronized operation. In addition, to keep the AC frequency fluctuation minor, the photovoltaic panels were designed to suppress the output fluctuation using storage batteries. As a result of case studies, the simulator predicts that the frequency fluctuation varies greatly depending on the weather conditions and the fluctuation suppression parameters, even for the same configuration with the same power generation capacity. Therefore, it is preferable to increase the moving average time of the output and reduce the feedback gain of the storage battery to suppress the output fluctuation from the photovoltaics. However, there is a tradeoff between suppressing the output fluctuation and the minimum required storage capacity. Furthermore, since the photovoltaics' output varies with the weather, other private generators' capacity and control parameters significantly impact power quality. The simulator proposed in this study makes it possible to study each hospital's desirable private generator configuration.
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