Abstract
At a global level, buildings constitute one of the most significant energy-consuming sectors. Current energy policies in the EU and the U.S. emphasize that buildings, particularly those in the residential sector, should employ renewable energy and storage and efficiently control the total energy system. In this work, we propose a Home Energy Management System (HEMS) by employing a Model-Based Predictive Control (MBPC) framework, implemented using a Branch-and-Bound (BAB) algorithm. We discuss the selection of different parameters, such as time-step, to employ prediction and control horizons and the effect of the weather in the system performance. We compare the economic performance of the proposed approach against a real PV-battery system existing in a household equipped with several IoT devices, concluding that savings larger than 30% can be obtained, whether on sunny or cloudy days. To the best of our knowledge, these are excellent values compared with existing solutions available in the literature.
Highlights
Over the last two decades the global electricity consumption market has been growing at an average yearly reported level of 3.1% [1], which causes extra stress on electrical power systems
We show that a significant reduction in the Model-Based Predictive Control (MBPC) time complexity with minimal impact on the performance can be obtained by employing a small control horizon (CH), achieving substantial cost savings, or improved gains, in comparison with many of the approaches found in the literature
The first analysis (Analysis 1) is focused on determining the impact of the prediction horizon and time step between the samples on the power fluxes determined by the MBPC as well as the computational time
Summary
Over the last two decades the global electricity consumption market has been growing at an average yearly reported level of 3.1% [1], which causes extra stress on electrical power systems. The cost-effectiveness of energy efficiency measures in this sector is always much lower than in services buildings, which are generally used by many more people In this context, it is of fundamental importance that the prevalence of the smart grid paradigm extends the impact capacity of the demand response. Storage, analysis, and reporting describes the activities related to collecting and processing data from different types of sensors, such as power meters or weather stations. These data can be used to monitor and evaluate the operation of the building and identify mismatches between the models in order to calibrate them or identify energy efficiency actions. For example, electric power, occupancy, smoke, light, and temperature [1]
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