Modeling and Optimization of Smart Building Energy Management System Considering Both Electrical and Thermal Load

Muhammad Hilal Khan, Azzam Ul Asar, Nasim Ullah, Fahad R. Albogamy and Muhammad Kashif Rafique
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Muhammad Hilal Khan: Department of Electrical Engineering, CECOS University of IT & Emerging Sciences, Peshawar 25000, Pakistan
Azzam Ul Asar: Department of Electrical Engineering, CECOS University of IT & Emerging Sciences, Peshawar 25000, Pakistan
Nasim Ullah: Department of Electrical Engineering, College of Engineering, Taif University KSA, P.O. Box 11099, Taif 21944, Saudi Arabia
Fahad R. Albogamy: Computer Sciences Program, Turabah University College, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
Muhammad Kashif Rafique: Department of Electrical Engineering, Balochistan University of Engineering and Technology, Khuzdar 89100, Pakistan

Energies, 2022, vol. 15, issue 2, 1-28

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.

Keywords: battery storage system (BSS); combined heat and power system (CHP); electric vehicle (EV); real coded genetic algorithm (RCGA); renewable energy system (RES); smart building energy management system (SBEMS) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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