Smart homes energy management: Optimal multi-objective appliance scheduling model considering electrical energy storage and renewable energy resources
As smart homes (SHs) integrate into distribution systems, microgrid scheduling has become increasingly important because of their schedulable loads that reduce peak loads. Accordingly, a multi-objective optimization approach is presented for SH energy management (SHEM) and demand response (DR) progr...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-02-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844025007972 |
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| Summary: | As smart homes (SHs) integrate into distribution systems, microgrid scheduling has become increasingly important because of their schedulable loads that reduce peak loads. Accordingly, a multi-objective optimization approach is presented for SH energy management (SHEM) and demand response (DR) programs with 30-min time slots. Time-of-use tariffs are used in the suggested scheme, and the primary goal is to minimize the daily bills and peak-to-average ratio (PAR), simultaneously. This scheme includes flexible and fixed home appliances. Here, the SHEM system consists of photovoltaic and wind turbine systems in combination with an electrical energy storage (EES) system to provide optimum peak load performance at peak times, based on the discharging and charging mechanism. Also, in the proposed mathematical formulation, the bought and selling energy is considered during the day. An improved Biogeography-based optimization algorithm (IBBO) is used to solve the multi-objective problem. The first step is to develop the equations for general electrical appliances of particular SH consumers, and then minimize the mentioned two objectives. Based on the outcomes under different scenarios such as different sizes of renewable energy resources, various charging/discharging rates, and different selling electricity tariff ratios, PAR and operational costs are reduced, and the electricity is sold to upstream. Moreover, simulations show that the suggested scheme produces the optimal outcomes, in which both objectives are near their optimal levels, as shown in the Pareto Front of the optimal solutions. The maximum standard deviation of total objective function between all cases for IBBO, gray wolf optimizer (GWO), and whale optimization algorithm (WOA) are 6.55, 17.22, and 24.87, respectively, which show the robustness of IBBO in finding the best solution in comparisons of other algorithms. Also, the average solution of IBBO is lower than GWO, and WOA, which shows the performance and superiority of IBBO in finding the best solution. |
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| ISSN: | 2405-8440 |