Title :
GA strategies for optimal planning of daily energy consumptions and user satisfaction in buildings
Author :
Pallotti, E. ; Mangiatordi, F. ; Fasano, M. ; Del Vecchio, P.
Author_Institution :
Electron. Dept., Univ. of Roma TRE, Rome, Italy
Abstract :
The electricity demand in the residential sector in western countries is expected to grow in the next decade, partly as a result of the progressive deployment of electric vehicles. To deal with this problem without installing additional generation capacity, resulting in a loss of efficiency and increased greenhouse gas emissions, it is necessary to design a system for the optimal planning of electricity consumption in the residential sector. Since the energy use in buildings is closely related to the activities of residents, the shaping of the load curve and the smoothing of the energy peak must not be done without compromising the user comfort. This paper investigates the use of an heuristic strategies to find the optimal planning of energy consumption inside every building in a neighborhood. The issue is formulated as multi-objective optimization problem aiming at reducing the peak load as well as minimizing the energy cost and the impact on the user satisfaction. The effectiveness of the approach is confirmed by simulation results carried out on a residential area with a variety of electrical devices. The simulations reveal that the proposed strategy is able to plan the daily energy consumptions of a great number of electrical devices with good performance in terms of computational cost.
Keywords :
building management systems; electric vehicles; energy consumption; load forecasting; optimisation; power system planning; buildings; daily energy consumptions; electric vehicles; electricity consumption; electricity demand; genetic algorithm; greenhouse gas emissions; multiobjective optimization problem; optimal planning; residential sector; user satisfaction; Electricity; Energy consumption; Genetic algorithms; Optimization; Planning; Sociology; Statistics; demand side management; energy conpsuntion scheduling; evolutionary algorithm; multiobjective optimizzation;
Conference_Titel :
Environment and Electrical Engineering (EEEIC), 2013 12th International Conference on
Conference_Location :
Wroclaw
Print_ISBN :
978-1-4673-3060-2
DOI :
10.1109/EEEIC.2013.6549556