Abstract
This paper presents the development of a heuristic-based algorithm for a Home Electric Energy Management System (HEEMS). The novelty of the proposal resides in the fact that solutions of the Pareto front, minimizing both the energy consumption and cost, are obtained by a Genetic Algorithm (GA) considering the renewable energy availability as well as the user activity level (AL) inside the house. The extensive solutions search characteristic of the GAs is seized to avoid the calculation of the full set of Pareto front solutions, i.e., from a reduced set of non-dominated solutions in the Pareto sense, an optimal solution with the best fitness is obtained, reducing considerably the computational time. The HEEMS considers models of the air conditioner, clothes dryer, dishwasher, electric stove, pool pump, and washing machine. Models of the wind turbine and solar PV modules are also included. The wind turbine model is written in terms of the generated active power exclusively dependent on the incoming wind profiles. The solar PV modules model accounts for environmental factors such as ambient temperature changes and irradiance profiles. The effect of the energy storage unit on the energy consumption and costs is evaluated adapting a model of the device considering its charge and discharge ramp rates. The proposed algorithm is implemented in the Matlab® platform and its validation is performed by comparing its results to those obtained by a freeware tool developed for the energy management of smart residential loads. Also, the evaluation of the performance of the proposed HEEMS is carried out by comparing its results to those obtained when the multi-objective optimization problem is solved considering weights assigned to each objective function. Results showed that considerable savings are obtained at reduced computational times. Furthermore, with the calculation of only one solution, the end-user interaction is reduced making the HEEMS even more manageable than previously proposed approaches.
Highlights
During the last decade, the development of smart grids (SGs) has increased exponentially empowered by the benefits of renewable energy resources [1]
In order to numerically evaluate the performance of the proposed Home Electric Energy Management System (HEEMS) under different scenarios, three types of tests are performed: the first test considers no solutions of the Pareto front are obtained and the behavior of the HVAC for a day of summer and winter seasons is compared to the results obtained with a freeware tool developed for the energy management of smart residential loads so-called Smart Residential Load Simulator (SRLS) [32]; the second test considers no solutions of the Pareto set are obtained and the energy consumption and costs are evaluated when renewable energy generation is available in the household
A heuristic algorithm for a HEEMS has been proposed in this paper
Summary
The development of smart grids (SGs) has increased exponentially empowered by the benefits of renewable energy resources [1]. (2) the HEMS are time-restricted for producing the necessary operational decisions; (3) the on-site renewable energy availability; (4) the users preferences and their activity inside the house; etc In this context, several approaches have proposed the development of a variety of such devices embedding a diversity of control strategies for load management. Based on the favorable results in all cases, it is shown how end users will significantly benefit when installing storage devices to shave load peaks under a Time-of-Use (TOU) tariff scenario It is noted in [5] that most of the optimization problems associated with HEMS have been solved using traditional optimization algorithms such as linear programming, non-linear programming, and dynamic programming.
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