Flow direction algorithm-based optimal power flow analysis in the presence of stochastic renewable energy sources

Maheshwari, Ankur; Sood, Yog Raj; Jaiswal, Supriya

doi:10.1016/j.epsr.2022.109087

Cited by 43 publications

(11 citation statements)

References 28 publications

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“…This study examines the effectiveness of the proposed Grey Wolf Optimization (GWO) algorithm by analyzing both standard and modified IEEE 30 and 118-bus systems. The necessary bus data, line data, and cost coefficients of thermal generators for the standard IEEE 30 and 118-bus systems were acquired from Maheshwari et al (2023a). The simulations were carried out using MATLAB software on a PC equipped with a 2.4 GHz processor and 8 GB of RAM.…”

Section: Resultsmentioning

confidence: 99%

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Social welfare maximization in deregulated power market incorporating wind power plants using metaheuristic algorithm

Raturi,

Jarial,

Sood

et al. 2023

Wind Engineering

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The increasing integration of renewable energy sources (RESs), particularly wind power plants (WPP), into deregulated power markets introduces complexities in optimizing social welfare (SW). This article proposes a recent metaheuristic algorithm to address this challenge and maximize SW while accounting for the presence of WPP and the inherent uncertainty associated with wind power forecasting. The proposed algorithm optimizes generation scheduling and demand-side bidding strategies in the deregulated power market to maximize SW while ensuring economic efficiency. To validate the effectiveness and robustness of the proposed algorithm, MATLAB simulations are conducted on IEEE 30 and IEEE 118-bus systems. The results demonstrate that the proposed algorithm provides promising solutions for maximizing SW, especially in the context of incorporating WPP. This research contributes to the advancement of power market optimization methods and promotes the seamless integration of RESs, fostering a more sustainable energy future.

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Section: Resultsmentioning

confidence: 99%

“…The power output of a wind turbine (WT) can be determined using the equation (1) for a given wind speed v (Maheshwari et al, 2023a):…”

Section: Problem Formulation and Optimization Techniquementioning

confidence: 99%

Social welfare maximization in deregulated power market incorporating wind power plants using metaheuristic algorithm

Raturi,

Jarial,

Sood

et al. 2023

Wind Engineering

Self Cite

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“…There are several algorithms for reducing the number of scenarios, such as k-means clustering, backward reduction, and fast forward selection. In this work, the simultaneous backward reduction algorithm [17][18][19] was employed to reduce a ternary scenario tree for the daily PV generations and load profiles.…”

Section: Scenario Generationmentioning

confidence: 99%

Dynamic Optimal Power Dispatch in Unbalanced Distribution Networks with Single-Phase Solar PV Units and BESS

Radosavljević¹,

Ktena²,

Gajic³

et al. 2023

Preprint

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The battery energy storage systems (BESS) are most promising solution for increasing efficiency and flexibility of distribution networks (DNs) with significant penetration level of photovoltaic (PV) systems. There are various issues related to the optimal operation of DNs with integrated PV sys-tems and BESS that need to be addressed to maximize DN performance. This paper deals with the day-ahead optimal active-reactive power dispatching in unbalanced DNs with integrated sin-gle-phase PV generation and BESSs. The objectives are the minimization of cost for electricity, en-ergy losses in the DN and voltage unbalance at three-phase load buses by optimal managing of active and reactive power flows. To solve this highly constrained nonlinear optimization problem, a hybrid particle swarm optimization with sigmoid-based acceleration coefficients (PSOS) and cha-otic gravitational search algorithm (CGSA), called PSOS-CGSA algorithm, is proposed. A scenario based approach encompassing the Monte Carlo simulation (MCS) method with the simultaneous backward reduction algorithm is used for the probabilistic assessment of the uncertainty of PV generation and power of loads. The effectiveness of the proposed procedure is evaluated through the series test cases in a modified IEEE 13-bus feeder.

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“…The Equilibrium Optimization (EO) proposed in [23] is a unique technique for solving the OPF problem that considers uncertainties caused by not only wind generation but also other components in the power grid. Other techniques proposed include the White Shark Optimizer (WSO) in [24], Circle Search Algorithm (CSA) in [25], Flow Direction Algorithm (FDA) in [26], Chaotic Bonobo Optimizer (CBO) in [27], Hybrid JAYA and Firefly algorithm in [28], and Equilibrium Optimizer (EO) [29].…”

Section: Literature Reviewmentioning

confidence: 99%

CAVOA: A chaotic optimization algorithm for optimal power flow with facts devices and stochastic wind power generation

Mohamed,

Kamel,

Hassan

et al. 2023

IET Generation Trans & Dist

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The study proposes a modified version of African Vultures Optimization Algorithm (AVOA) to address the optimal power flow (OPF) issue. The developed optimizer is called Chaotic AVOA (CAVOA). Prior to applying CAVOA to the problem at hand, its accuracy is evaluated using 23 benchmark functions. The production powers of WTs that are interrupted are predicted using Weibull probability density functions (PDFs). Two additional costs, namely penalty cost and reserve cost, are incorporated into the goal function of the OPF. The simulation results of CAVOA are compared with those of the original AVOA to solve the OPF. The proposed OPF technique and its solution methodology are verified on the IEEE 30‐bus test system, taking into account the flexible alternating current transmission systems (FACTS) devices, which have several benefits such as reducing active power transmission loss, regulating power flow, and improving voltage stability/profile. The research's simulation results demonstrate that CAVOA is more effective in identifying the OPF's optimal solution by reducing the overall power cost and power losses. The results indicate that the CAVOA algorithm outperforms AVOA as a metaheuristic optimization algorithm due to its superior capability in addressing challenging OPF problems with a minimal convergence rate. For example, when comparing the two algorithms, CAVOA achieved a significant improvement. It successfully reduced the cost function by approximately 0.023% in Case I and 0.035% in Case II, while also decreasing power loss by 2.57% in Case II and 0.57% in Case I. Additionally, CAVOA exhibited a remarkable reduction of 5.36% in voltage deviation compared to AVOA in Case I. Furthermore, in Case III, CAVOA demonstrated a decrease of 0.64% in gross cost when compared to AVOA.

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Flow direction algorithm-based optimal power flow analysis in the presence of stochastic renewable energy sources

Cited by 43 publications

References 28 publications

Social welfare maximization in deregulated power market incorporating wind power plants using metaheuristic algorithm

Social welfare maximization in deregulated power market incorporating wind power plants using metaheuristic algorithm

Dynamic Optimal Power Dispatch in Unbalanced Distribution Networks with Single-Phase Solar PV Units and BESS

CAVOA: A chaotic optimization algorithm for optimal power flow with facts devices and stochastic wind power generation

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