The absence of electricity is among the gravest problems preventing a nation’s development. Hybrid renewable energy systems (HRES) play a vital role to reducing this issue. The major goal of this study is to use the non-dominated sorting genetic algorithm (NSGA)-II and hybrid optimization of multiple energy resources (HOMER) Pro Software to reduce the net present cost (NPC), cost of energy (COE), and CO2 emissions of proposed power system. Five cases have been considered to understand the optimal HRES system for Kutubdia Island in Bangladesh and analyzed the technical viability and economic potential of this system. To demonstrate the efficacy of the suggested strategy, the best case outcomes from the two approaches are compared. The study’s optimal solution is also subjected to a sensitivity analysis to take into account fluctuations in the annual wind speed, solar radiation, and fuel costs. According to the data, the optimized PV/Wind/Battery/DG system (USD 711,943) has a lower NPC than the other cases. The NPC obtained by the NSGA-II technique is 2.69% lower than that of the HOMER-based system.

The integration of renewable energy sources into the power grid poses several challenges due to their variability in output power as they primarily depend on weather conditions. As a countermeasure, battery energy storage systems are introduced in order to mitigate output power fluctuations of the renewable energy sources. Moreover, demand response programs have been attracting huge attention as an effective mechanism for efficient energy management. It enables power suppliers to cope with the output uncertainty of renewable energy systems. First step, we propose an HRES design methodology that takes into account one year. In order to find the optimal power supply configuration, it is necessary to take into account the changes in load demand due to weather conditions and seasons for one year. However, the computational cost of simulating one year is huge and requires a rather long simulation time. Therefore, we propose a simplified simulation method that uses clustering to account for changes in load demand due to weather and seasonality in one year. Second, the first proposed method is used to propose a method for optimizing the HRES equipment capacity and operation schedule considering demand response. In this paper, mixed-integer linear programing is used as the optimization method. The proposed method considers two types of demand response and achieves cost reduction by controlling load demand in response to generated power fluctuations.

This study explores Bangladesh’s present energy condition, renewable energy (RE) possibilities and designs an optimal 100% RE-based off-grid power system for St. Martin’s Island, Bangladesh. The optimal size of a hybrid renewable microgrid based on photovoltaic (PV) cells, a battery energy storage system (BESS), fuel cells (FC), and an electrolysis plant (EP) is proposed. Advanced direct load control (ADLC) and rooftop PV meet the energy demand at the lowest cost, and profits are maximized by selling chemical products produced by seawater electrolysis. Four cases are explored with the mixed-integer linear programming (MILP) optimization technique using MATLAB® software to demonstrate the efficacy of the suggested power system. The system cost in case 1 is lower than in the other cases, but there is no chance of profiting. Cases 2, 3, and 4 have greater installation costs, which may be repaid in 8.17, 7.72, and 8.01 years, respectively, by the profits. Though the revenue in case 3 is 6.23% higher than in case 2 and and 3.85% higher than in case 4, case 4 is considered the most reliable power system, as it can meet the energy demand at the lowest cost while increasing profits and not putting a burden on customers.

Optimal sizing of the power system can drastically reduce the total cost, which is challenging due to the fluctuation in output power of RE (primarily wind and solar) and pollution from thermal generators. The main purpose of this study is to cope with this output power uncertainty of renewables by considering ADLC, residential PV, and BESS at the lowest cost and with the least amount of carbon emission, while putting less burden on consumers by minimizing the IL. This paper optimizes the cost and carbon emission function of a hybrid energy system comprising PV, WG, BESS, and DG at Aguni Island, Japan, using a multi-objective optimization model. To solve the proposed problem in the presence of ADLC, the ϵ-constraint method and MILP are utilized. After obtaining all possible solutions, the FSM selects the best possible solution among all solutions. The result shows that while case 1 has a lower energy cost than the other cases, the quantity of IL is quite significant, putting customers in a burden. In case 2 and case 3, the total energy cost is 11.23% and 10% higher than case 1, respectively, but the sum of the IL is 99% and 95.96% lower than case 1 as the ADLC is applied only for the consumers who have residential PV and BESS, which can reflect the importance of residential PV and BESS. The total cost of case 3 is 1.72% lower than case 2, but IL is higher because sometimes home PV power will be used to charge the home BESS.

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