Abstract
Energy consumption in buildings is expected to increase by 40% over the next 20 years. Electricity remains the largest source of energy used by buildings, and the demand for it is growing. Building energy improvement strategies is needed to mitigate the impact of growing energy demand. Introducing a smart energy management system in buildings is an ambitious yet increasingly achievable goal that is gaining momentum across geographic regions and corporate markets in the world due to its potential in saving energy costs consumed by the buildings. This paper presents a Smart Building Energy Management system (SBEMS), which is connected to a bidirectional power network. The smart building has both thermal and electrical power loops. Renewable energy from wind and photo-voltaic, battery storage system, auxiliary boiler, a fuel cell-based combined heat and power system, heat sharing from neighboring buildings, and heat storage tank are among the main components of the smart building. A constraint optimization model has been developed for the proposed SBEMS and the state-of-the-art real coded genetic algorithm is used to solve the optimization problem. The main characteristics of the proposed SBEMS are emphasized through eight simulation cases, taking into account the various configurations of the smart building components. In addition, EV charging is also scheduled and the outcomes are compared to the unscheduled mode of charging which shows that scheduling of Electric Vehicle charging further enhances the cost-effectiveness of smart building operation.
Highlights
The worldwide electrical energy consumption of the building sector which includes both residential and commercial buildings is about 20% of the total energy produced [1]
This research work has mainly focused on designing and modeling an smart building energy management system (SBEMS) in the context of modern smart buildings to optimize their economic operation by utilizing real coded genetic algorithm (RCGA) which demonstrates the efficient usage of renewable energy resources in conjunction with grid connected energy resources which can potentially create a new ecosystem that can rely more on renewable energy, saving energy cost, minimising energy waste, and slashing carbon emissions
The system was optimized using a real coded genetic algorithm which gives optimal scheduling of hybrid energy resources to minimize the cost of 24-h energy consumption
Summary
The worldwide electrical energy consumption of the building sector which includes both residential and commercial buildings is about 20% of the total energy produced [1]. The rapid increase in global electrical energy demand and its generation from conventional resources, along with the increasing integration of intermittent and inexhaustible renewable energy resources to the electricity network called for enhancement and updating of the existing electrical grid infrastructure to obtain efficient, reliable, and clean energy [9] This emerges the concept of the smart grid from which the consumer can intelligently manage their energy consumption [10]. A certain number of distributed energy resources (DER) components are connected to a large scale residential building connected to a bidirectional utility grid is considered These components consist of a photo voltaic and wind turbine installation, a fuel cell based CHP system, a battery storage system (BSS), and a number of EVs used for day to day work-related trips. The SBEMS system can be accessed from anywhere, both outside and inside the building, by using appropriate software via internet and mobile communication
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