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

Fernando Lezama, Ricardo Faia, Pedro Faria and Zita Vale
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Fernando Lezama: Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD), Polytechnic of Porto (ISEP/IPP), 4200-072 Porto, Portugal
Ricardo Faia: Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD), Polytechnic of Porto (ISEP/IPP), 4200-072 Porto, Portugal
Pedro Faria: Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD), Polytechnic of Porto (ISEP/IPP), 4200-072 Porto, Portugal
Zita Vale: Polytechnic of Porto (ISEP/IPP), 4200-072 Porto, Portugal

Energies, 2020, vol. 13, issue 10, 1-18

Abstract: 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.

Keywords: demand response; energy service provider; energy storage system; evolutionary algorithms; optimization; photovoltaic generation (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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