Implementation of a building energy management system for residential demand response

2017 ◽  
Vol 55 ◽  
pp. 100-110 ◽  
Author(s):  
Sergi Rotger-Griful ◽  
Ubbe Welling ◽  
Rune Hylsberg Jacobsen
Keyword(s):  
Energy Management ◽  
Demand Response ◽  
Management System ◽  
Building Energy ◽  
Energy Management System ◽  
Building Energy Management ◽  
Residential Demand

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

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 574
Author(s):  
Muhammad Hilal Khan ◽  
Azzam Ul Asar ◽  
Nasim Ullah ◽  
Fahad R. Albogamy ◽  
Muhammad Kashif Rafique
Keyword(s):  
Energy Management ◽  
Management System ◽  
Energy Demand ◽  
Storage System ◽  
Building Energy ◽  
Energy Management System ◽  
Smart Building ◽  
Building Energy Management ◽  
Real Coded Genetic Algorithm ◽  
The Impact

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.

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