Cloud Energy Storage Management Under Building Thermal Comfort and Net Load Seasonal Uncertainty Scenario

Residential and small commercial users face growing challenges with the variability in photovoltaic (PV) power generation and extreme load fluctuations, influenced by factors like building thermal conditions, the number of occupants inside buildings, and changing weather. A shared energy storage systems behind the smart meters present a proactive solution, offering these users enhanced flexibility to optimize their energy usage. In this paper, cloud energy storage architecture is managed under the user's building thermal comfort and PV power generation uncertainty scenario. A hardware module is developed using ESP32 microcontroller and PZEM004T meter components to collect energy consumption data. The RS-485 communication interface protocol is used in the hardware module. A data-driven net demand error forecast-based strategy has also been developed to minimize the PV power and load uncertainty effect, including outdoor temperature. The particle swarm optimization algorithm optimizes the human thermal comfort set point. The price-based scheduling strategy is used to maximize user utilization. The numerical results with Indian grid price under uncertainty show that CES architecture service is more economical for users than grid-connected supply.