A Different Perception of Hybrid Renewable Energy Sources Integrated Multi-objective Optimal Power Flow considering Performance Parameters and Penetration

ABSTRACT

Traditional generating units, as well as renewable energy resources, make up the electrical grid. The proposed article proposes performance indices for optimal power flow, which combine wind turbines, solar photovoltaic systems, and hybrid solar with small hydropower sources. The irregularity of renewable energy sources’ performance adds to the complexity of the optimal power flow (OPF) problem. The analytical strategies also use lognormal, Weibull, and Gumbel probability density functions to approximate the energy yield of those renewables. Also explored is the effect of changing distribution parameters and the penetration of renewable energy resources as a function of optimal power flow. Penalty charges for underestimation and standby charges for overestimation of unusual non-conventional generating units are included in the objective feature. The optimization problem is solved using a non-dominated multi-objective moth flame optimization technique. In terms of achieving diverse and convergent Pareto optimal solutions, the MOMFO optimizer is more efficient and robust than the SMODE/SF and MOEA/D-SF optimizers, according to the simulation outcomes. As a result IEEE-30 bus system, the MOMFO optimizer can be used to tackle the MO-SCOPF issue with the incorporation of wind, solar, hydro, and thermal generators in an integrated multiple-power system.

GRAPHICAL ABSTRACT

KEYWORDS:

• Wind power units
• meta-heuristics
• multi-objective optimal power flow
• solar PV energy
• small hydropower
• moth flame optimization

List Of Nomenclature

OPFOptimal Power Flow

MFOMoth Flame Optimisation

GWOGrey Wolf Optimisation

MVOMulti-Verse Optimisation

IMOIon Motion Optimisation

TGThermal Generating unit

WGWind Generation

PVPhoto Voltaic

SPHHybrid solar power and a small-hydel power unit

ISOIndependent System Operator

PDFProbability Density Function

BCSBest Compromise Solution

MOMFOMulti-Objective Moth Flame Optimisation

MOOPFMulti-Objective Optimal Power Flow

$P_{TGi}$Power output of $i^{th}$thermal unit.

$P_{ws}$Scheduled power from the wind power unit

$P_{ss}$Scheduled power from a solar PV unit

$P_{ssh}$Scheduled power from hybrid solar power and small hydel power unit

$P_{wav}$Actually offered power from wind unit

$P_{sav}$Actually offered power from a solar PV unit

$P_{shav}$Actually offered power from hybrid solar power and small hydel power unit

$g_w$Direct charge constant for wind unit

$h_s$Direct charge constant for a solar PV unit

$m_h$Direct charge constant for small hydro unit

$K_{Rw}$Reserve charge constant for overestimation of wind unit

$K_{Pw}$Penalty charge constant for underestimation of wind unit

$K_{Rs}$Reserve charge constant for overestimation of the solar PV unit

$K_{Ps}$Penalty charge constant for underestimation of the solar PV unit

$K_{Rsh}$Reserve charge constant for overestimation of hybrid solar PV and small hydel unit

$K_{Psh}$Penalty charge constant for underestimation of hybrid solar PV and small hydel unit

$C_{tax}$Carbon tax ($/Tonne)

$Gs$Solar irradiance ($\mathrm{W}/{\mathrm{m}}^2$)

$Q_w$River flow rate ($m^3/s$)

$f_v\left(v\right)$Probability of wind speed $v$ m/s

$f_G\left(Gs\right)$Probability of solar irradiance $G\mathrm{W}/{\mathrm{m}}^2$

$f_Q\left(Q_w\right)$Probability of river flow rate $Q_w$

$p_{wr}$Rated generated power from a wind unit

$P_{sr}$Rated generated power from the solar PV unit

$P_{hr}$Rated generated power from the small hydro plant

$\alpha ,\beta$Scale and shape factors of Weibull PDF respectively

$\mu ,\sigma$Mean and standard deviation of Lognormal PDF respectively

$\lambda ,\gamma$Location and scale parameters of Gumbel PDF respectively

$P_{loss}$Active power loss in the grid

$VD$Accumulative voltage deviance in a grid

Disclosure Statement

No potential conflict of interest was reported by the author(s).