Optimal planning DG and BES units in distribution system considering uncertainty of power generation and time-varying load

Khasanov, Mansur; Kamel, Salah; Awad, Ayman; Jurado, Francisco

doi:10.3906/elk-2003-46

Cited by 12 publications

(12 citation statements)

References 28 publications

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“…It is considered that the probabilistic nature of solar radiation follows the Beta PDF [46, 47]. The Beta PDF of solar radiations (kW/m 2 ) in the time interval ‘ t ’is defined as Equation (4):

\begin{matrix} true \\ right f_{b} (s^{t}) = ({, \begin{matrix} 0true \frac{normalΓ false(α^{t} + β^{t} false)}{normalΓ (α^{t}) normalΓ (β^{t})} {s^{t}}^{false(α^{t} - 1 false)} {false(1 - s^{t} false)}^{false(β^{t} - 1 false)}, & 0 \leq s^{t} \leq 1, α^{t}, β^{t} \geq 0 \\ 0, & otherwise \end{matrix}) \end{matrix}

…”

Section: Pv Wt Biomass Bes and Load Modelingmentioning

confidence: 99%

“…PV and BES units are located on the same bus to avoid energy losses in the charge state of BES. The daily energy amount of PV+BES unit for all the duration (T = 24 h) on bus k is expressed as [22,23,46]:…”

Section: Pv Sizingmentioning

confidence: 99%

“…It is considered that the probabilistic nature of solar radiation follows the Beta PDF [46,47]. The Beta PDF of solar radiations (kW/m 2 ) in the time interval 't'is defined as Equation ( 4):…”

Section: Solar Radiation Modellingmentioning

confidence: 99%

“…The optimal BES unit size at bus k is calculated based on the nominal power (

P_{B E S_{k}}

) and the energy capacity (

E_{B E S_{k}}

) as well as it can hold all the excess a PV unit energy, which reduces the power losses for each time interval at the minimum. Maximum charge and discharge over a specific duration during the 24‐hour cycle of the day is used to determine BES unit nominal power [22, 23, 46]. The maximum energy of charge during this period is used to identify BES unit energy capacity.…”

Section: Mathematical Problem Formulationmentioning

confidence: 99%

“…After calculating the PLSFs of all buses, they are sorted in descending order. The buses with higher PLSF are considered more suitable for installing PV, WT, Biomass, and BESs units (up to 50% of system buses) [44][45][46].…”

Section: Power Loss Sensitivity Factormentioning

confidence: 99%

See 4 more Smart Citations

Optimal distributed generation and battery energy storage units integration in distribution systems considering power generation uncertainty

Khasanov

Kamel

Rahmann

et al. 2021

IET Generation Trans & Dist

Self Cite

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This paper proposes an application of the recent metaheuristic rider optimization algorithm (ROA) for determining the optimal size and location of renewable energy sources (RES) including wind turbine (WT), photovoltaic (PV), and biomass-based Distributed Generation (DG) units in distribution systems (DS). The main objective function is to minimize the total power and energy losses. Power loss-sensitivity factor (PLSF) is used with the ROA to determine the suitable candidate buses and accelerate the solution process. The Weibull and Beta probability distribution functions (PDF) are employed to characterize the variability of wind speed and solar radiation, respectively. The high penetration of intermittent renewable resource together with demand variations has introduced many challenges to distribution systems such as power fluctuations, voltage rise, high losses, and low voltage stability, therefore battery energy storage (BES) and dispatchable Biomass are considered to smooth out the fluctuations and improve supply continuity. The standard 33 and 69-bus test systems are used to verify the effectiveness of the proposed technique compared with other well-known optimization techniques. The results show that the developed approach accelerates to the near-optimal solution seamlessly, and in steady convergence characteristics compared with other techniques.

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\begin{matrix} true \\ right f_{b} (s^{t}) = ({, \begin{matrix} 0true \frac{normalΓ false(α^{t} + β^{t} false)}{normalΓ (α^{t}) normalΓ (β^{t})} {s^{t}}^{false(α^{t} - 1 false)} {false(1 - s^{t} false)}^{false(β^{t} - 1 false)}, & 0 \leq s^{t} \leq 1, α^{t}, β^{t} \geq 0 \\ 0, & otherwise \end{matrix}) \end{matrix}

…”

Section: Pv Wt Biomass Bes and Load Modelingmentioning

confidence: 99%

Section: Pv Sizingmentioning

confidence: 99%

Section: Solar Radiation Modellingmentioning

confidence: 99%

“…The optimal BES unit size at bus k is calculated based on the nominal power (

P_{B E S_{k}}

) and the energy capacity (

E_{B E S_{k}}

Section: Mathematical Problem Formulationmentioning

confidence: 99%

Section: Power Loss Sensitivity Factormentioning

confidence: 99%

See 3 more Smart Citations

Optimal distributed generation and battery energy storage units integration in distribution systems considering power generation uncertainty

Khasanov

Kamel

Rahmann

et al. 2021

IET Generation Trans & Dist

Self Cite

View full text Add to dashboard Cite

show abstract

An improved moth flame optimization for optimal DG and battery energy storage allocation in distribution systems

Elseify,

Kamel,

Nasrat

2024

Cluster Comput

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Deploying distributed generators (DGs) powered by renewable energy poses a significant challenge for effective power system operation. Optimally scheduling DGs, especially photovoltaic (PV) systems and wind turbines (WTs), is critical because of the unpredictable nature of wind speed and solar radiation. These intermittencies have posed considerable challenges to power grids, including power oscillation, increased losses, and voltage instability. To overcome these challenges, the battery energy storage (BES) system supports the PV unit, while the biomass aids the WT unit, mitigating power fluctuations and boosting supply continuity. Therefore, the main innovation of this study is presenting an improved moth flame optimization algorithm (IMFO) to capture the optimal scheduling of multiple dispatchable and non-dispatchable DGs for mitigating energy loss in power grids, considering different dynamic load characteristics. The IMFO algorithm comprises a new update position expression based on a roulette wheel selection strategy as well as Gaussian barebones (GB) and quasi-opposite-based learning (QOBL) mechanisms to enhance exploitation capability, global convergence rate, and solution precision. The IMFO algorithm's success rate and effectiveness are evaluated using 23rd benchmark functions and compared with the basic MFO algorithm and other seven competitors using rigorous statistical analysis. The developed optimizer is then adopted to study the performance of the 69-bus and 118-bus distribution grids, considering deterministic and stochastic DG's optimal planning. The findings reflect the superiority of the developed algorithm against its rivals, emphasizing the influence of load types and varying generations in DG planning. Numerically, the optimal deployment of BES + PV and biomass + WT significantly maximizes the energy loss reduction percent to 68.3471 and 98.0449 for the 69-bus's commercial load type and to 54.833 and 52.0623 for the 118-bus's commercial load type, respectively, confirming the efficacy of the developed algorithm for maximizing the performance of distribution systems in diverse situations.

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Probabilistic optimal planning of multiple photovoltaics and battery energy storage systems in distribution networks: A boosted equilibrium optimizer with time-variant load models

Elseify,

SeyyedAbbasi,

Domínguez-García

et al. 2023

Journal of Energy Storage

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Optimal planning DG and BES units in distribution system considering uncertainty of power generation and time-varying load

Cited by 12 publications

References 28 publications

Optimal distributed generation and battery energy storage units integration in distribution systems considering power generation uncertainty

Optimal distributed generation and battery energy storage units integration in distribution systems considering power generation uncertainty

An improved moth flame optimization for optimal DG and battery energy storage allocation in distribution systems

Probabilistic optimal planning of multiple photovoltaics and battery energy storage systems in distribution networks: A boosted equilibrium optimizer with time-variant load models

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