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Article

Demand Response of Residential Houses Equipped with PV-Battery Systems: An Application Study Using Evolutionary Algorithms

1
Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD), Polytechnic of Porto (ISEP/IPP), 4200-072 Porto, Portugal
2
Polytechnic of Porto (ISEP/IPP), 4200-072 Porto, Portugal
*
Author to whom correspondence should be addressed.
Energies 2020, 13(10), 2466; https://doi.org/10.3390/en13102466
Received: 12 April 2020 / Revised: 6 May 2020 / Accepted: 9 May 2020 / Published: 14 May 2020
(This article belongs to the Special Issue Computational Intelligence Applications in Smart Grid Optimization)
Households equipped with distributed energy resources, such as storage units and renewables, open the possibility of self-consumption of on-site generation, sell energy to the grid, or do both according to the context of operation. In this paper, a model for optimizing the energy resources of households by an energy service provider is developed. We consider houses equipped with technologies that support the actual reduction of energy bills and therefore perform demand response actions. A mathematical formulation is developed to obtain the optimal scheduling of household devices that minimizes energy bill and demand response curtailment actions. In addition to the scheduling model, the innovative approach in this paper includes evolutionary algorithms used to solve the problem under two optimization approaches: (a) the non-parallel approach combine the variables of all households at once; (b) the parallel-based approach takes advantage of the independence of variables between households using a multi-population mechanism and independent optimizations. Results show that the parallel-based approach can improve the performance of the tested evolutionary algorithms for larger instances of the problem. Thus, while increasing the size of the problem, namely increasing the number of households, the proposed methodology will be more advantageous. Overall, vortex search overcomes all other tested algorithms (including the well-known differential evolution and particle swarm optimization) achieving around 30% better fitness value in all the cases, demonstrating its effectiveness in solving the proposed problem. View Full-Text
Keywords: demand response; energy service provider; energy storage system; evolutionary algorithms; optimization; photovoltaic generation demand response; energy service provider; energy storage system; evolutionary algorithms; optimization; photovoltaic generation
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MDPI and ACS Style

Lezama, F.; Faia, R.; Faria, P.; Vale, Z. Demand Response of Residential Houses Equipped with PV-Battery Systems: An Application Study Using Evolutionary Algorithms. Energies 2020, 13, 2466. https://doi.org/10.3390/en13102466

AMA Style

Lezama F, Faia R, Faria P, Vale Z. Demand Response of Residential Houses Equipped with PV-Battery Systems: An Application Study Using Evolutionary Algorithms. Energies. 2020; 13(10):2466. https://doi.org/10.3390/en13102466

Chicago/Turabian Style

Lezama, Fernando, Ricardo Faia, Pedro Faria, and Zita Vale. 2020. "Demand Response of Residential Houses Equipped with PV-Battery Systems: An Application Study Using Evolutionary Algorithms" Energies 13, no. 10: 2466. https://doi.org/10.3390/en13102466

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