Strategies for Managing Cool Thermal Energy Storage with Day-ahead PV and Building Load Forecasting at a District Level

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Virginia Tech

In hot climate areas, the electrical load in a building spikes, but not by the same amount daily due to various conditions. In order to cover the hottest day of the year, large cooling systems are installed, but are not fully utilized during all hot summer days. As a result, the investments in these cooling systems cannot be fully justified.

A solution for more optimal use of the building cooling system is presented in this dissertation using Cool Thermal Energy Storage (CTES) deployed at a district level. Such CTES systems are charged overnight and the cool charge is dispatched as cool air during the day. The integration of the CTES helps to downsize the otherwise large cooling systems designed for the hottest day of the year. This reduces the capital costs of installing large cooling systems. However, one important question remains - how much of the CTES should be charged during the night, such that the cooling load for the next day is fully met and at the same time the CTES charge is fully utilized during the day.

The solution presented in this dissertation integrated the CTES with Photovoltaics (PV) power forecasting and building load forecasting at a district level for a more optimal charge/discharge management. A district comprises several buildings of different load profiles, all connected to the same cooling system with central CTES. The use of forecasting for both the PV and the building cooling load allows the building operator to more accurately determine how much of the CTES should be charged during the night, such that the cooling system and CTES can meet the cooling demand for the next day. Using this approach, the CTES would be optimally sized, and utilized more efficiently during the day. At the same time, peak load savings are achieved, thus benefiting an electric utility company.

The district presented in this dissertation comprises PV panels and three types of buildings – a mosque, a clinic and an office building. In order to have a good estimation for the required CTES charge for the next day, reliable forecasts for the PV panel outputs and the electrical load of the three buildings are required. In the model developed for the current work, dust was introduced as a new input feature in all of the forecasting models to improve the models' accuracy. Dust levels play an important role in PV output forecasts in areas with high and variable dust values.

The overall solution used both the PV panel forecasts and the building load forecasts to estimate the CTES charge for the next day. The presented method was tested against the baseline method with no forecasting system. Multiple scenarios were conducted with different cooling system sizes and different CTES capacities. Research findings indicated that the presented method utilized the CTES charge more efficiently than the baseline method. This led to more savings in the energy consumption at the district level.

Machine learning, Artificial Intelligence, Smart Grids, Solar Forecasting, Energy Storage, Cool Thermal Energy Storage, Thermal Energy Storage