4 equipment provided by MIT Facilities and rudimentary control strategies for chiller balancing and HRSG firing. Gas demand is converted to emissions according to a standard emissions factor of 182 kgCO2eq/MWh. The total electricity demand due to the new district heating and cooling networks are computed using constant coefficients of performance for the high-temperature loop scenario and coefficients of performance which vary according to hourly net thermal demand in the ambient loop scenario: heating efficiency improves in the summer while cooling efficiency worsens, and vice versa. In both district demand scenarios, additional energy necessary to balance borefield temperatures is computed according to annual net thermal demand: heat-injection is required in heating dominated years while heat-rejection required in cooling dominated years. Electricity requirements of the district heating and cooling networks are met first by local carbon-free generation capacity if available, and then grid-imported electricity. When applicable, thermal demand is first decreased by any local thermal generation capacity (nuclear micro-reactors or deep geothermal). Grid emissions factors and costs are computed hourly, while gas cost data is computed annually. Dashboard The dashboard visualizes the decarbonization pathways and aims to allow a larger audience to interact with the results. The simulated (over 56,000) decarbonization pathways can be explored through the developed interactive dashboard. This tool is designed to showcase the combined effect of various technologies on the campus emissions and facilitate extracted insight for decision making. The main view presents an aggregate emissions line plot which captures data from over 56,000 scenarios. Utilizing Java and D3.js, this section allows users to interactively explore different emission outcomes over time (from now till 2050) through a combination of preset and customizable scenarios. This is displayed in an interactive line plot. At the top of the dashboard, a dynamic filtering interface, lets users select and combine technologies to see their implementation extent (baseline, partial or full). Users can choose preset scenarios that are hard coded to plot the emission trajectories of the “Business as usual” scenario or “Best practice implementation”. The user can also build their own scenario through recording the generated lines from their explorations. The activated filters influence the display of schematic campus designs in the bottom pane, aligning with the selected technologies. Each layout dynamically adjusts to reflect the chosen scenarios, providing a visual understanding of the potential emissions impact by 2050. Additionally, the filtering mechanisms leverages Java's robust capabilities in handling complex data operations to enable real-time filtering of scenarios based on key metrics such as emissions, cost, risk, and innovation. This part of the dashboard uses D3.js for visual interactivity, enhancing user engagement through brushing and linking techniques that highlight the relationships within the data. The cost, innovation and risk metrics will be integrated once data is available in the future.
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