Windows provide occupants with natural light and a view of the outside, enhancing productivity, which is important as people spend approximately 90% of their time indoors. This is especially the case during and after the COVID-19 pandemic. Automated controls for window shading systems can be used to control solar radiation and daylight entering the space. Lighting controls can reduce lighting requirements, providing energy savings and better visual comfort for occupants than manual controls, which are seldom used effectively. Past studies have explored automated lighting and shading control strategies, and reported energy savings and visual comfort improvements over their baselines. However, the assumptions for baseline models differ across different studies, making it difficult to compare these automated controls. Thus, this research uses a multi-step modeling process, including daylighting and energy simulations using RADIANCE and EnergyPlus, respectively (i) to compare existing control strategies using the same building inputs (baseline model) for a prototypical small office building, (ii) to develop and evaluate the effectiveness of a novel integrated control strategy that uses variables such as occupancy, HVAC state, solar radiation entering the space, time of day for control, and others variables. (iii) to develop a parametric model to investgate the impact of different input variables such as building form factor, window-to-wall ratio for all different orientations, shade properties such as openness factor, and shade overhang depth on energy performance and visual comfort. On top of improving energy efficiency and visual comfort in buildings, managing demand at the grid level is becoming more important as renewable energy gets added to the generation mix. Instead of adding more generation to balance the grid, usually using new fossil fuel-based generation, the other approach to balance the grid is to use existing building loads and reduce their demand during specific hours (also known as demand-side Flexibility Services (FS)). As buildings become smarter with the adoption of new technologies for sensing and control, more integration between buildings and the electric grid is possible. Building loads such as air conditioning and lighting in commercial buildings have the potential to provide demand-side FS. In particular, demand-side flexibility using lighting loads is not well studied in the literature. In commercial buildings, lighting accounts for approximately 10-15% of the load at any time. Past studies have shown that lighting can be dimmed by 15-20% without causing visual discomfort to the occupants. The forth objective thus of theis study (iv) if to improve the existing literature by providing building level and grid level estimates for using lighting loads for all the common commercial building types as demand-side Flexible Services (FS) for three future scenarios in the Midwest region.
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