An Optimized Setpoint Framework for Energy Flexible Buildings in Hot Desert Climates

In Dubai, the rapid increase in electricity demand calls for investment in developing more efficient and enhanced future energy networks. There has been a yearly increase of 10% in both energy and power demand in 2021 compared to 2020. To accommodate the constant growth of energy demand and related carbon emissions, Dubai is implementing an ambitious plan to increase its share of renewables. The Dubai Clean Energy Strategy aims to provide 25% of power output from clean energy by 2030 and 100% by 2050. In addition, Dubai Electricity and Water Authority’s initiatives, such as the Mohammed bin Rashid Al Maktoum (MBR) Solar Park and Shams Dubai distributed generation program, have significantly increased solar energy production. However, solar power is intrinsically variable and could affect the stability of the energy system, especially when it accounts for a high percentage of the total generation. In Dubai, buildings are major energy consumers accounting for 80% of the total electricity consumption, and their share is expected to grow due to accelerated urbanization. Energy Flexible Buildings can respond quickly to the grid’s dynamic needs. Therefore, it is crucial to evaluate their implementation feasibility and capability to support the stability of the power grid. This chapter presents a study that utilized thermal models to determine the energy flexibility potential of a building in Dubai. The investigation was carried out using a gray-box resistance-capacitance model of the building. This model was validated against a detailed reference model developed in EnergyPlus. Then, it was used to study different energy flexibility strategies for the building. Two indicators including storage capacity and storage efficiency were utilized to quantify the energy flexibility for a typical day in each month of the year. However, it was observed that there is no significant difference in energy flexibility values between different months of the year. The model was then used to perform a predictive control study of different grid signals for the marginal cost of electricity. It was found that implementing the model predictive control strategies could result in 11% cost savings.