Stochastic optimal power flow in islanded DC microgrids with correlated load and solar PV uncertainties

Reddy, O. Yugeswar; Jithendranath, J.; Chakraborty, Ajoy Kumar; Guerrero, Josep M.

doi:10.1016/j.apenergy.2021.118090

Cited by 15 publications

(7 citation statements)

References 45 publications

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“…In this paper, DC power flow model (Yugeswar et al, 2022) is applied to analyze the power system. In addition, the node power balance model and the branch power flow model are shown in Eqs.…”

Section: Electricity Networkmentioning

confidence: 99%

A Double-Deck Deep Reinforcement Learning-Based Energy Dispatch Strategy for an Integrated Electricity and District Heating System Embedded with Thermal Inertial and Operational Flexibility

Zhang

Liu

2022

SSRN Journal

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Section: Electricity Networkmentioning

confidence: 99%

A Double-Deck Deep Reinforcement Learning-Based Energy Dispatch Strategy for an Integrated Electricity and District Heating System Embedded with Thermal Inertial and Operational Flexibility

Zhang

Liu

2022

SSRN Journal

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“…China is actively addressing the goals of peaking carbon emissions and achieving carbon neutrality [1]. To achieve this, it is crucial to develop an electricity system that adapts to the increasing share of new energy sources, which is also an intrinsic requirement for ensuring the national energy security.…”

Section: Introductionmentioning

confidence: 99%

Consideration of Multi-Objective Stochastic Optimization in Inter-Annual Optimization Scheduling of Cascade Hydropower Stations

Jia,

Zhang,

Zhou

et al. 2024

Energies

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There exists a temporal and spatial coupling effect among the hydropower units in cascade hydropower stations which constitutes a complex planning problem. Researching the multi-objective optimization scheduling of cascade hydropower stations under various spatiotemporal inflow impacts is of significant importance. Previous studies have typically only focused on the economic dispatch issues of cascade hydropower stations, with little attention given to their coupling mechanism models and the uncertainty impacts of inflows. Firstly, this paper establishes a coupled optimization scheduling model for cascade hydropower stations and elaborates on the operational mechanism of cascade hydropower stations. Secondly, according to the needs of actual scenarios, two types of optimization objectives are set, considering both the supply adequacy and peak-shaving capacity as indicators, with the total residual load and the peak-valley difference of the residual load as comprehensive optimization objectives. Subsequently, considering the uncertainty impact of the inflow side, a stochastic optimization model for inflow is established based on a normal distribution probability. Finally, case study analyses demonstrate that the proposed model not only effectively achieves supply stability but also reduces the peak-valley difference in load, and can achieve optimized scheduling under the uncertain environment of inflow.

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“…In general, many scholars have been successfully applied various stochastic approaches to address the power system issues including adaptive constraint differential evolution (ACDE) algorithm 2 , an improved version of the coyote optimization algorithm (COA) 3 , teaching-learning-based optimizer (TLBO) 4 , adaptive multiple teams perturbation-guiding Jaya (AMTPG-Jaya) 5 , backtracking search algorithm (BSA) 6 , crisscross search based grey wolf optimizer (CS-GWO) 7 , ant colony optimization (ACO) 8 , effective whale optimization algorithm (EWOA) 9 , moth swarm algorithm (MSA) 10 , adaptive group search optimization (AGSO) 11 , improved colliding bodies optimization (ICBO) 12 , differential search algorithm (DSA) 13 , invasive weed optimization (IWO) 14 , interior search algorithm (ISA) 15 , robust optimization approach (Rao) 16 , Salp swarm algorithm (SSA) 17 . Stud krill herd algorithm (SKH) 18 , symbiotic organisms search algorithm (SOS) 19 , tree-seed algorithm (TSA) 20 , Hunter-prey optimization (HPO) 21 , particle swarm optimization (PSO) 22 , fuzzy-based improved comprehensive-learning particle swarm optimization (FBICLPSO) algorithm 23 , hybrid Grey wolf optimizer and particle swarm optimization (GWO-PSO) 24 , hybrid of the firefly and PSO algorithms (HFAPSO) 25 , combined genetic algorithm and particle swarm algorithm (GA-PSO) 26 , multi objective genetic algorithm (MOGA) 27 , artificial bee colony algorithm based on a non-dominated sorting genetic approach (ABC-NSGA-II) 28 , fitness-distance balance based-TLABC (teaching-learning-based artificial bee colony) (FDB-TLABC) 29 , non-dominated sorting culture differential evolution algorithm (NSCDE) 30 , differential evolution algorithm based on state transition of specific individuals (DE-TSA) 31 , multi-objective covariance matrix adaptation evolution strategy (CMA-ES) 32 , manta ray foraging optimization (MRFO) 33 , 34 , dragonfly algorithm (DA) 35 , flower pollination algorithm (FPA) 36 , etc.…”

Section: Introductionmentioning

confidence: 99%

A leader supply-demand-based optimization for large scale optimal power flow problem considering renewable energy generations

Daqaq,

Hassan,

Kamel

et al. 2023

Sci Rep

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The supply-demand-based optimization (SDO) is among the recent stochastic approaches that have proven its capability in solving challenging engineering tasks. Owing to the non-linearity and complexity of the real-world IEEE optimal power flow (OPF) in modern power system issues and like the existing algorithms, the SDO optimizer necessitates some enhancement to satisfy the required OPF characteristics integrating hybrid wind and solar powers. Thus, a SDO variant namely leader supply-demand-based optimization (LSDO) is proposed in this research. The LSDO is suggested to improve the exploration based on the simultaneous crossover and mutation mechanisms and thereby reduce the probability of trapping in local optima. The LSDO effectiveness has been first tested on 23 benchmark functions and has been assessed through a comparison with well-regarded state-of-the-art competitors. Afterward, Three well-known constrained IEEE 30, 57, and 118-bus test systems incorporating both wind and solar power sources were investigated in order to authenticate the performance of the LSDO considering a constraint handling technique called superiority of feasible solutions (SF). The statistical outcomes reveal that the LSDO offers promising competitive results not only for its first version but also for the other competitors.

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Stochastic optimal power flow in islanded DC microgrids with correlated load and solar PV uncertainties

Cited by 15 publications

References 45 publications

A Double-Deck Deep Reinforcement Learning-Based Energy Dispatch Strategy for an Integrated Electricity and District Heating System Embedded with Thermal Inertial and Operational Flexibility

A Double-Deck Deep Reinforcement Learning-Based Energy Dispatch Strategy for an Integrated Electricity and District Heating System Embedded with Thermal Inertial and Operational Flexibility

Consideration of Multi-Objective Stochastic Optimization in Inter-Annual Optimization Scheduling of Cascade Hydropower Stations

A leader supply-demand-based optimization for large scale optimal power flow problem considering renewable energy generations

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