Hybrid Harris Hawks optimizer for integration of renewable energy sources considering stochastic behavior of energy sources

Abstract

Summary Renewable energy sources powered distributed generation (RES‐DG) is getting more indispensable to encounter the considerable increase in demand for electric energy owing to its techno‐economic benefits and eco‐friendly nature. An economic solution to this demand can only be obtained with the optimal placement and sizing of RES‐DGs. The optimal siting and sizing of RES‐DG, such as Photovoltaic (PV) and Wind Turbine (WT) is still a hot topic due to the uncertainties in solar irradiance (SI) and wind speed (WS). The main objective of this research paper is to develop a RES‐DG siting and sizing strategy for the discrete, nonlinear siting and sizing pattern of RES‐DGs using a novel hybrid Harris' Hawk optimizer (HHHO), considering the stochastic nature of SI and WS. The Weibull and Beta probability density functions (PDFs) are utilized for modeling the stochastic nature of WS and SI, respectively. The optimization of the multiobjective function comprises active power loss reduction, enhancement in voltage profile, and improvement in voltage stability index (VSI). Different scenarios of single and multiple RES‐DGs and capacitor banks (CB) are examined to validate the efficiency of the proposed novel HHHO based RES‐DGs siting and sizing strategy. The results show a considerable reduction in power loss, enhancement in the system voltage profile, and improvement in VSI. Evaluation of results by comparing withstate‐of‐art hybrid algorithms shows that the proposed solution using HHHO algorithm is globally optimum.

Hybrid Wind/PV/Battery Energy Management-Based Intelligent Non-Integer Control for Smart DC-Microgrid of Smart University

“…Content may change prior to final publication. U denotes the control signal, 3 D and 4 D denotes dynamics uncertainty in the energy stage parameters as given by Figure 3. Here also, according to the state of the storage system, the PV conversion system can be operated under MPPT for maximum power extraction or off-MPPT for power balance as shown in Figs.…”

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Optimal location and sizing of multiple distributed generators in radial distribution network using metaheuristic optimization algorithms

“…Recently, several techniques and algorithms have been implemented to find a solution for the problem of the optimal integration of the PVDG units in RDN considering different objective functions as: Applied Biogeography-Based Optimization (BBO) algorithm in [6], Moth Flame Optimization (MFO) algorithm in [7], Adaptive PSO (APSO) algorithm in [8], Sinusoidal Modulated PSO (SM-PSO) algorithm in [9], Spider Monkey Optimization (SMO) algorithm in [10], Artificial Bee Colony (ABC) algorithm in [11], Improved Artificial Bee Colony (IABC) algorithm in [12], applied the Grasshopper Optimization Algorithm (GOA) in [13], Gravitational Search Algorithm (GSA) in [14], Coyote Optimization Algorithm (COA) in [15], and used Krill Herd Algorithm (KHA) in [16]. Recently, applied Sine-Cosine Algorithm (CSA) in [17], Salp Swarm Algorithm (SSA) in [18], Harris Hawk Optimizer (HHO) algorithm in [19], Improved Equilibrium Optimization Algorithm (IEOA) in [20], Chaotic Grey Wolf Optimization (CGWO) algorithm in [21], Bat Algorithm (BA) in [22], and applied Ant Lion Optimizer (ALO) algorithm in [23].…”

Supporting distributed energy resources with optimal placement and sizing of voltage regulators on the distribution system by an improved teaching‐learning‐based optimization algorithm

“…The placement and sizing AVRs have faced more attention among them because of the economic superiority. Therefore, the scholars have studied this critical optimization problem to find the best optimization algorithm 3,4 …”