Precise models of photovoltaic (PV) modules are crucial for simulating PV system characteristics. To address the challenges of accurately and promptly acquiring parameters from measured current-voltage (I-V) data of P...
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Precise models of photovoltaic (PV) modules are crucial for simulating PV system characteristics. To address the challenges of accurately and promptly acquiring parameters from measured current-voltage (I-V) data of PV modules, an improved artificialecosystemoptimization (IAEO) algorithm was proposed. The IAEO algorithm enhanced the producer diversity within the production operator by introducing a probabilistic selection mechanism and an evolution mechanism. Moreover, a mutation operation based on historical values was introduced into the consumption operator to enhance the search ability. A comparative analysis was conducted among IAEO and the other six algorithms for parameter extraction of PV cells/modules using publicly available datasets. The IAEO algorithm demonstrated high identification accuracy and speed. The identification accuracy was improved by constructing a two-layer optimization structure with Newton-Raphson method and using the double-diode model. The Newton-Raphson method integrated IAEO algorithm achieved root mean squared error (RMSE) values of 7.32392x10-4, 1.67191x10-3, 9.89002x10-3, and 1.48362x10-3 for France cell, STM6-40, STP6-120, and PWP 201, respectively. An I-V tester was designed to measure the I-V data of different PV modules on the experimental platform. Results showed that the IAEO algorithm exhibited superior performance in extracting four specific PV modules, and the relative error of power is below 2.5%.
With emergence of automated environments, energy demand increased with unexpected ratio, especially total electricity consumed in the residential sector. This unexpected increase in demand in energy brings a challengi...
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With emergence of automated environments, energy demand increased with unexpected ratio, especially total electricity consumed in the residential sector. This unexpected increase in demand in energy brings a challenging task of maintaining the balance between supply and demand. In this work, a robust artificialecosystem-inspired optimizer based on demand-side management is proposed to provide the optimal scheduling pattern of smart homes. More precisely, the main objectives of the developed framework are: i) Shifting load from on-peak hours to off-peak hours while fulfilling the consumer intends to reduce electricity-bills. ii) Protect users comfort by improving the appliances waiting time. artificialecosystem optimizer (AEO) algorithm is a novel optimization technique inspired by the energy flocking between all living organisms in the ecosystem on earth. Demand side management (DSM) program is modeled as an optimization problem with constraints of starting and ending of appliances. The proposed optimization technique based DSM program is evaluated on two different pricing schemes with considering two operational time intervals (OTI). Extensive simulation cases are carried out to validate the effectiveness of the proposed optimizer based energy management scheme. AEO minimizes total electricity-bills while keeping the user comfort by producing optimum appliances scheduling pattern. Simulation results revealed that the proposed AEO achieved a minimization electricity-bill up to 10.95, 10.2% for RTP and 37.05% for CPP for the 12 and 60 min operational time interval (OTI), respectively, in comparison to other results achieved by other optimizers. On the other hand peak to average ratio (PAR) is reduced to 32.9% using RTP and 31.25% using CPP tariff.
optimization of reactive power dispatch (ORPD) problem is a key factor for stable and secure operation of the electric power systems. In this paper, a newly explored nature-inspired optimization through artificial eco...
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optimization of reactive power dispatch (ORPD) problem is a key factor for stable and secure operation of the electric power systems. In this paper, a newly explored nature-inspired optimization through artificialecosystemoptimization (AEO) algorithm is proposed to cope with ORPD problem in large-scale and practical power systems. ORPD is a well-known highly complex combinatorial optimization task with nonlinear characteristics, and its complexity increases as a number of decision variables increase, which makes it hard to be solved using conventional optimization techniques. However, it can be efficiently resolved by using nature-inspired optimizationalgorithms. AEO algorithm is a recently invented optimizer inspired by the energy flocking behavior in a natural ecosystem including non-living elements such as sunlight, water, and air. The main merit of this optimizer is its high flexibility that leads to achieve accurate balance between exploration and exploitation abilities. Another attractive property of AEO is that it does not have specific control parameters to be adjusted. In this work, three-objective version of ORPD problem is considered involving active power losses minimization and voltage deviation and voltage stability index. The proposed optimizer was examined on medium- and large-scale IEEE test systems, including 30 bus, 118 bus, 300 bus and Algerian electricity grid DZA 114 bus (220/60 kV). The results of AEO algorithm are compared with well-known existing optimization techniques. Also, the results of comparison show that the proposed algorithm performs better than other algorithms for all examined power systems. Consequently, we confirm the effectiveness of the introducing AEO algorithm to relieve the over losses problem, enhance power system performance, and meet solutions feasibility. One-way analysis of variance (ANOVA) has been employed to evaluate the performance and consistency of the proposed AEO algorithm in solving ORPD problem.
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