Abstract
Electrification of remote rural areas by adopting renewable energy technologies through the advancement of smart micro-grids is indispensable for the achievement of continuous development goals. Satisfying the electricity demand of consumers while adhering to reliability constraints with docile computation analysis is challenging for the optimal sizing of a Hybrid Energy System (HES). This study proposes the new application of an Artificial Ecosystem-based Optimization (AEO) algorithm for the optimal sizing of a HES while satisfying Loss of Power Supply Probability (LPSP) and Renewable Energy Fraction (REF) reliability indices. Furthermore, reduction of surplus energy is achieved by adopting Demand Side Management (DSM), which increases the utilization of renewable energy. By adopting DSM, 28.38%, 43.05%, and 65.37% were achieved for the Cost of Energy (COE) saving at 40%, 60%, and 80% REF, respectively. The simulation and optimization results demonstrate the most cost-competitive system configuration that is viable for remote-area utilization. The proposed AEO algorithm is further compared to Harris Hawk Optimization (HHO) and the Future Search Algorithm (FSA) for validation purpose. The obtained results demonstrate the efficacy of AEO to achieve the optimal sizing of HES with the lowest COE, the highest consistent level, and minimal standard deviation compared with HHO and FSA. The proposed model was developed and simulated using the MATLAB/code environment.
Highlights
The reduction of conventional energy sources coupled with increasing global warming have accelerated the growth of renewable energy sources (RES) such as solar and wind [1]
By running the algorithms for 30 runs, a unique distinct pattern is obtained for the algorithms, which implies that the parameters always unite at a definite location
This paper presents a new application of Artificial Ecosystem-based Optimization (AEO) in the optimal sizing of a stand-alone Hybrid Energy System (HES)
Summary
The reduction of conventional energy sources coupled with increasing global warming have accelerated the growth of renewable energy sources (RES) such as solar and wind [1]. Cost saving is the major purpose of the load-shifting process [28] This method will be utilized in this study for the optimal sizing of the HES. The evaluation of three algorithms: Artificial Ecosystem-based Optimization (AEO) [30], Future Search Algorithm (FSA) [31], Harris Hawk Optimization (HHO) [32], were evaluated for optimal sizing These algorithms were analyzed for optimizing Cost of Energy (COE) at zero Loss of Power Supply Probability (LPSP). The rest of the paper is arranged as follows: Section 2 describes the Al Sulaymaniyah site and load; Section 3 proposes the HES configuration; Section 4 discusses the AEO algorithm; Section 5 describes the power management approach; Section 6 introduces the developed DSM; Section 7 describes the reliability indices; Section 8 illustrates the objective function; Section 9 discusses the results; and Section 10 presents the conclusion
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