Supervisor control and optimization of multi-sources pumping system with battery storage

Serir, Chafiaa; Rekioua, Djamila; Mezzai, Nabil; Bacha, Seddik

doi:10.1016/j.ijhydene.2016.05.096

Cited by 40 publications

(17 citation statements)

References 18 publications

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“…36,37 A connection between total head, efficiency, flow rate is demonstrated by Equation (9). The total of static head and frictional head loss is the total head and the frictional head loss which is expressed in Equation ( 10); moreover, Equation (11) demonstrates the friction factor. The model of PHS is shown in Figure 1.…”

Section: Formulation Of Pumped Hydro Storagementioning

confidence: 99%

“…This prompts the requirement for an ideal hybrid system energy management strategy. [7][8][9][10][11] Hydrokinetic innovation is a promising sustainable power source innovation that has been appeared to produce altogether preferred and less expensive electricity over solar and wind systems. 7,8 In contrast to conventional hydroelectric generation, hydrokinetics need not bother with a water charge to create electricity.…”

Section: Introductionmentioning

confidence: 99%

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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

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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...

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Section: Formulation Of Pumped Hydro Storagementioning

confidence: 99%

Section: Introductionmentioning

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

“…This connection is done by through power converters, more specifically through voltage source inverters (VSIs) . Thus, it is essential to implement a proper control technique or the VSI that allows keeping the quality of electrical energy and maintaining its characteristics at a point of common coupling (PCC) within allowable margins presented in the standards . In this work, we are focused on wind‐power renewable energy.…”

Section: Introductionmentioning

confidence: 99%

STA and SOSM control‐based approach of a renewable power generator for adjusting grid frequency and voltage

Krim

Abbes

Krim

et al. 2020

Int Trans Electr Energ Syst

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Summary This article is interested in a controlled three‐phased voltage source inverter (VSI) for a grid‐connected wind power generator. The objective consists of participating in auxiliary services by enhancing the grid stability. An active wind generator associated with a battery/super‐capacitor storage system composes the studied renewable generator. It is related to a power consumer (load) to form a microgrid (MG) that can work according to grid stability in two modes: standalone and grid‐tied. Thus, the principal objective of this study is to develop a robust control method for MG connection with the grid to ensure the grid voltage and frequency regulation. This control has two control tasks: First, an adaptive droop control is implemented to adjust power flows exchanged with the grid to ensure its stability. It regulates its voltage and frequency. Second, a sliding mode control (SMC) with super‐twisting algorithm is put forward for the VSI to improve the regulation of the grid voltage and frequency under uncertainties. It improves control results concerning the reduction of harmonics caused by sudden variations of the load and of powers exchanged with the grid. It allows preventing the phenomenon of chattering created by classical sliding mode technique. Extensive simulation studies are carried out under MATLAB/Simulink. They prove the effectiveness of the suggested droop and SMCs compared with the classical proportional‐integral controller.

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“…Moreover, it also handles the non-linearity problem [8] [9]. In 2016, Chafiaa Serir et al [10] used fuzzy logic control to monitor and optimize the multi-source pumping system with the storage battery. Their work focused on system stability to protect the battery from overcharging and deep discharge.…”

Section: Introductionmentioning

confidence: 99%

Reliability of the MPPT Control on the Energy Parameters of a Photovoltaic Generator

Jeannot¹,

Jacques²,

Jeannot³

2020

WJET

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This article describes a technique that allows a photovoltaic (PV) production unit to obtain the maximum power at all times. Here, we use the MPPT control via fuzzy logic on a DC/DC boost-type converter. In order to achieve our goals, we first proceeded to model a PV panel. The resulting model offers the possibility to better account for the influence of different physical quantities such as temperature, irradiation, series resistance, shunt resistance and diode saturation current. Thus, the maximum power to be provided by the PV system is acquired by fuzzification and defuzzification of the input and output variables of the converter. Subsequently, a virtual model of an 800 Watt PV prototype is implemented in the Matlab environment. The simulation results obtained and presented, show the feasibility and efficiency of the proposed technology. Indeed, for a disturbance caused by a variation in brightness, our system guarantees the maximum stable power after 1.4 s. While for a load variation, the maximum power is continuous.

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Supervisor control and optimization of multi-sources pumping system with battery storage

Cited by 40 publications

References 18 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

STA and SOSM control‐based approach of a renewable power generator for adjusting grid frequency and voltage

Reliability of the MPPT Control on the Energy Parameters of a Photovoltaic Generator

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