The presence of harmonics in solar Photo Voltaic (PV) energy conversion system results in deterioration of power quality. To address such issue, this paper aims to investigate the elimination of harmonics in a solar fed cascaded fifteen level inverter with aid of Proportional Integral (PI), Artificial Neural Network (ANN) and Fuzzy Logic (FL) based controllers. Unlike other techniques, the proposed FLC based approach helps in obtaining reduced harmonic distortions that intend to an enhancement in power quality. In addition to the power quality improvement, this paper also proposed to provide output voltage regulation in terms of maintaining voltage and frequency at the inverter output end in compatible with the grid connection requirements. The simulations are performed in the MATLAB / Simulink environment for solar fed cascaded 15 level inverter incorporating PI, ANN and FL based controllers. To exhibit the proposed technique, a 3 kWp photovoltaic plant coupled to multilevel inverter is designed and hardware is demonstrated. All the three techniques are experimentally investigated with the measurement of power quality metrics along with establishing output voltage regulation.
In this manuscript, a hybrid approach for energy management in grid connected MG system is proposed. The grid connected MG system has photovoltaic (PV), wind turbine (WT), micro turbine (MT), battery. The proposed hybrid approach is the consolidation of both the Gradient Boosting Decision Trees (GBDT) and Sandpiper Optimization Algorithm (SOA) and this way the proposed technique is named as GBDT-SOA. Here, at grid connected microgrid configuration the required load demand is ever monitored by the GBDT approach. The perfect combination of the MG is optimized by SOA considering the predicted load requirement. The fuel cost including grid power hourly power variation, operation and maintenance cost of the grid connected microgrid system is defined as the objective of the proposed technique. The constraints are power demand, renewable energy sources, state of charge of storage elements. Batteries have been used as an energy source, to stabilize and allow the renewable power system units to maintain running in a steady, stable output power. At that point, the proposed model is executed in MATLAB/ Simulink work site and the performance is analyzed with existing techniques, such as BFO, SOA and SSA. The efficiency of the sources like photovoltaic, wind turbine, micro turbine, and battery using proposed technique is 95.9375%, 92.113%, 94.387% and 93.7560%.
This article proposes a hybrid optimization technique for optimal location and sizing of electric vehicle fast charging stations (EVFCSs) and renewable energy sources (RESs). The proposed hybrid optimization technique is the consolidation of recalling-enhanced recurrent neural network (RERNN) and Marine Predators Algorithm (MPA), hence it is called RERNN-m2MPA technique.Here, an enhanced MPA (m2MPA) is proposed. The major objective of this article is to energy loss reduction, voltage deviation of the power system network and minimization of the land cost with maximum weightage to serve maximum EV with minimum installation cost. The lessening of voltage Abbreviations: F l , load force; F r , rolling resistance force; F acc , acceleration force; S, area; m ev , m ess , mass of EV and ESS; ψ, solar irradiance random variable; μ, σ, mean and SD of solar irradiance; F f , fill factor; i z , output of solar module temperature current; i MPP , V module current at the maximum power point; i SC , short circuit current; t, solar module temperature; t a , ambient temperature; i SC , short circuit current of the module; S I , scale index; T, temperature of electrolyte; T f , electrolyte freezing temperature; i b , battery current; i dis , average current of the discharge battery module; D D , Deviation distance; i B , branch current; n B , total branch present in the network; V Bus T ð Þ, bus voltage at time period T; ABC, phases; W g , weightage of the location of EVCS; L c , total cost of all the EVCS; EV De Ve , required energy of EV at Vth vehicle; C Bat Ve , battery capacity of EV at Vth vehicle; SOC Arrival Ve , SOC Depart Ve , arrival SOC and departure SOC of Vth vehicle; E Ra Ve , electric range of Vth vehicle; D ET , each trip distance; D Rate , discharging rate in G2V mode; l EV Bus , load of EVCS; EV Arrival Ve
Summary This article proposes a multiobjective optimization model for renewable energy sources (RESs) and load demands uncertainty consideration for optimal design of hybrid combined cooling, heating, and power systems (CCHP). The hybrid CCHP system contains turbine, photovoltaic/thermal collectors, cooler/heater, supply setup, battery, and tank storage. The proposed hybrid method is the joint implementation of Garra Rufa Fish Optimization (GRFO) and Student Psychology Optimization Algorithm (SPOA); hence, it is named GRFO‐SPOA approach. An energy converters energy‐hub model along storage devices considers the properties of component of off‐design. The uncertainty of solar radiation together with building loads is exhibited at parametric manner and probability distributions. Assuming the uncertainty with system reliability and hybrid CCHP is enhanced to attain the feasible energetic, economic, and environmental benefit utilizing the GRFO‐SPOA method. To predict a new set, the GRFO‐SPOA method utilizes current datasets in the uncertainty modeling. The decision variables involves capacity of gas turbine and photovoltaic/thermal collectors, capacity of battery with water storing tank, and operational ratio of heat pump. The proposed method is implemented in MATLAB/Simulink; its efficiency is analyzed with other existing methods, like GA, SSA, and TSA technique. Once the confidence level of the system diminishes as of 0.99 to 0.50, the hybrid CCHP likened with traditional separate production system saves on average 13.7% of main energy and lessens 80% of acid gas emissions carbonic. The annual value saving rate is decreased because the confidence level of the system decreases and the uncertainty maximizes. The sensitivity analysis of the economist frontiers is executed on key economic parameters; therefore, it is obtained as an outcome of annual value saving rate is very sensitive to the value of fossil fuels, and the value of inversion of the star collectors has a stronger impact than that of turbine. The fist‐order statistical evaluation parameters, like mean, median, and SD, at 100 iterations for proposed technique is 0.61038, 0.5317, and 0.00543. Computation time utilizing 100, 150, 200, 250, and 500 trails of proposed technique is 48.1740 seconds, 51.2133 seconds, 71.0483 seconds, 60.00126 seconds, and 57.80132 seconds.
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