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  • ÀúÀÚVictor J. Gutierrez-Martinez, Carlos A. Moreno-Bautista, Jose M. Lozano-Garcia, Alejandro Piz
  • ÃâÆÇ»ç¾ÆÁø
  • ÃâÆÇÀÏ2020-07-12
  • µî·ÏÀÏ2020-12-21
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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.

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Á¦ 2Æí : ¿¬±¸³í¹®
A Heuristic Home Electric Energy Management System Considering
Renewable Energy Availability

1. Introduction 52
2. Optimization Model 54
3. Multi-Objective Genetic Algorithm 59
4. Results 61
5. Conclusions 68
6. References 69

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