Optimal power flow control of hybrid renewable energy system with energy storage: A WOANN strategy

Venkatesan, Karunakaran; Uma, G.

doi:10.1063/1.5048446

Cited by 52 publications

(33 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The novelty of the proposed approach was the combined execution of the MDA with BSA named as MDABSA technique. Venkatesan and Govindarajan 30 have illustrated an optimal control technique for power flow control of hybrid renewable energy systems (HRESs) like a combined photovoltaic and wind turbine system with energy storage. The proposed optimal control technique was the joined execution of both the whale optimization algorithm (WOA) and the artificial neural network (ANN).…”

Section: Recent Research Work: a Brief Reviewmentioning

confidence: 99%

Efficient energy management of hybrid renewable energy sources‐based smart‐grid system using a hybrid IDEA‐CFA technique

Kommula

Song

Liang

et al. 2021

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

A hybrid technique-based energy management scheme for optimal sizing of solar, wind, battery along the integral of pumped hydro storage (PHS) is presented in this paper. The suggested control scheme is the consolidated implementation of both Improved Dolphin Echolocation Algorithm (IDEA) and Cuttlefish Algorithm (CFA). Searching behavior of Dolphin Echolocation Algorithm (DEA) is changed through utilizing productive search capacities such as levy flight, so it is known as IDEA. The prominent intension of this work is the List of symbols and abbreviations: P rat , rated output power of solar PV; G h , hourly solar radiation (W/m 2 ); α p , temperature of power coefficient; T s , standard test conditions under PV cell temperature; s, scale factor along condition of r > 0 as well as s > 0; R, random variable of wind speed; v Rated , wind speed at rated; v o , cut-out speed of wind; R a , internal resistance; C i , incipient capacitance; R b , terminal ohmic resistance; C PV , C WT , C PHS , C BESS , annualized total cost of PV, WT, PHS, BESS; C gs , total price of electricity sold to the grid ($/yr) annually; C PHS AC rep ð Þ , annualized replacement cost of PHS; C MT Cap , initial capital cost of PHS; β, lifetime as years; y, lifetime of the PHS device in years; P HP , PHS nominal power; β j , externality cost of emission type; C i , fuel consumption rate of generating unit; M, emission types; P G , grid output power; P PV , output power of PV; P Ls , transmission losses; i, j, generator index; P max bc , P max bd , maximal power to charge as well as discharge the battery; P max l , upper limit of transmission power; P Gmax (l), maximum generating power of PHS unit; P Pmax (l), maximum pumping power of the PHS unit; Q Pmin (l), Q Pmax (l), minimum and maximum water discharge of PHS unit at pumping mode; V Umin (l), V Umax (l), PHS unit minimal and maximal volume of the upper reservoir; V U (l, t), V L (l, t), PHS unit upper and lower reservoir volume at hour t; V U0 (l), V L0 (l), initial volume of PHS unit upper as well as lower reservoir; M, load demand; f i (c), annualized capital cost; Φ ij , accumulative fitness; ⊕, entry-wise multiplications; R, random number between (0, 1); f, numbers of inputs; {C 13 , C 18 , C 30 }, from unselected costs, costs are randomly selected; IDEA, Improved Dolphin Echolocation Algorithm; DEA, Dolphin Echolocation Algorithm; CDF, cumulative distribution function; HRES, hybrid renewable energy sources; ANFIS-EHO, adaptive neuro fuzzy interference system with elephant herding optimization; PV, photovoltaic; BESS, battery energy storage system; f loss , loss factor of solar PV; G s , standard incident radiation (1000 W/m2); T c , current step time in the PV cell temperature; r, radio factor; v, wind speed; P Rated , rated output power of wind; v i , cut-in speed of wind; P w , wind output power; V oc , open circuit voltage; C p , polarization capacitance; AC, annualized cost of the system; C gp , total price ($/yr) of electricity purchased from grid annually; C PHS AC Cap ð Þ , annual...

show abstract

Section: Recent Research Work: a Brief Reviewmentioning

confidence: 99%

Efficient energy management of hybrid renewable energy sources‐based smart‐grid system using a hybrid IDEA‐CFA technique

Kommula

Song

Liang

et al. 2021

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…The ANN-based energy management for a stand-alone PV system is shown in [20]; the presented method reduced stress on the battery and improve its life span by introducing supercapacitor efficiently. In [21], optimal power flow control in PV Wind hybrid system integrated with a battery is achieved by combining the ANN and Whale optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Control Strategy to Enhance Power Smoothing of Renewable based Microgrid with Hybrid Energy Storage

Joshi¹

2021

TURCOMAT

View full text Add to dashboard Cite

A stand-alone renewable based microgrid (MG) performance with a hybrid energy storage system has been examined in this work. Stand-alone MG system mainly consists of a solar photovoltaic (PV) and permanent magnet synchronous generator (PMSG) based wind system. The hybrid energy storage system is based on Ni-Metal- Hydride (NiMH) battery and a supercapacitor (SC). The paper's primary goal is to propose an artificial neural network (ANN) based control strategy for charging/discharging control of Ni-Metal- Hydride battery & supercapacitor. The proposed maximum power tracking techniques (MPPT) include perturb and observe (P& O) algorithm for solar PV system while optimum torque (OT) MPPT for PMSG based wind turbine. The ANN-based control mechanism can maintain the DC bus voltage constant and trigger the supercapacitor to limit the battery current when the battery charging/ discharging current reached its threshold value. The proposed model responds quickly to intermittent nature PV-wind power generation or load power variation.

show abstract

“…Energy is a major contributor to environmental problems. Since burning fossil fuels for power generation and transportation is a substantial contributor of greenhouse gases to the atmosphere, various efforts have been made to replace conventional power sources with renewable energy sources such as wind power, solar power, tidal power, geothermal power, and hydro‐power along with cleaner fuels from natural gas 4,5 …”

Section: Introductionmentioning

confidence: 99%

“…The main goal was to control the power flow due to parameter variations in the source and load sides. The authors in Reference 4 have simulated power flow optimal control of the hybrid renewable energy system. A well‐organized hybrid method was the joint implementation as an optimal optimization algorithm and artificial neural network.…”

Section: Introductionmentioning

confidence: 99%

Anovel power management strategies inPV‐wind‐based grid connected hybrid renewable energy system using proportional distribution algorithm

Merahi

Badoud

Mekhilef

2021

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary This paper proposes a new management algorithm and operation of a hybrid renewable energy system (HRES) connected to the power system. The whole hybrid system is managed in such a manner that it can produce as much needed by the grid system. The proposed algorithm is used to distribute proportionally the active and reactive power references to the PV source and wind generators according to their ability contribution. Based on the available active powers and the ability on reactive power of each sub‐system, the references are calculated using a proportional distribution algorithm and sent individually by the principal controller to each auxiliary controller. The analysis of the simulation results obtained under Matlab/Simulink shows the effectiveness of the proposed management algorithm and the flexibility of the hybrid renewable energy system studied.

show abstract

Optimal power flow control of hybrid renewable energy system with energy storage: A WOANN strategy

Cited by 52 publications

References 35 publications

Efficient energy management of hybrid renewable energy sources‐based smart‐grid system using a hybrid IDEA‐CFA technique

Efficient energy management of hybrid renewable energy sources‐based smart‐grid system using a hybrid IDEA‐CFA technique

Intelligent Control Strategy to Enhance Power Smoothing of Renewable based Microgrid with Hybrid Energy Storage

Anovel power management strategies inPV‐wind‐based grid connected hybrid renewable energy system using proportional distribution algorithm

Contact Info

Product

Resources

About