Gravitational search algorithm applied to the comparative analysis of optimization models for demand response

• Grant number: 15/12599-0
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: September 01, 2015
• End date: August 31, 2017
• Field of knowledge: Engineering - Electrical Engineering - Power Systems
• Principal Investigator: Ricardo Augusto Souza Fernandes
• Grantee: Guilherme Spavieri
• Host Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil

Abstract

This research project has as main objective the performance evaluation of the Gravitational Search Algorithm for energy saving, i.e., the determination of electricity consumption strategies that will minimize costs for residential consumers. In order to better model such optimization problem, it will be considered an extensive literature analysis. During this analysis, it will considered models that describes the consumption and cost of electricity according to the loads (used over a certain period of time) and micro-generations (photovoltaic panels, wind generators, among other) that may be present in a smart home. This way, Portuguese electricity management models will be considered, because they are better suited to the current Brazilian scenario. Therefore, we intend to minimize the cost of electricity for final consumers based on an optimized planning of loads and energy sources used by the smart home at different times. In addition, it will be considered a variable cost of electricity over time, which is a factor that differs from the current Brazilian energy policy (as occurs in countries like Canada that have the Time of Use). In this sense, the cost of electricity will be discretized throughout the day and also depending on the time of year. Finally, the results will be evaluated and the performance of Gravitational Search Algorithm for different optimization models may be analyzed. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

SPAVIERI, G.; CAVALCA, D. L.; FERNANDES, R. A. S.; LAGE, G. G.; RUTKOWSKI, L; SCHERER, R; KORYTKOWSKI, M; PEDRYCZ, W; TADEUSIEWICZ, R; ZURADA, JM. An Adaptive Individual Inertia Weight Based on Best, Worst and Individual Particle Performances for the PSO Algorithm. ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, ICAISC 2018, PT I, v. 10841, p. 12-pg., 2018-01-01. (15/12599-0)

CAVALCA, DIEGO L.; SPAVIERI, GUILHERME; FERNANDES, RICARDO A. S.; RUTKOWSKI, L; SCHERER, R; KORYTKOWSKI, M; PEDRYCZ, W; TADEUSIEWICZ, R; ZURADA, JM. Comparative Analysis Between Particle Swarm Optimization Algorithms Applied to Price-Based Demand Response. ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, ICAISC 2018, PT I, v. 10841, p. 10-pg., 2018-01-01. (15/12599-0)