Information gap decision theory based OPF with HVDC connected wind farms

Rabiee, Abbas; Soroudi, Alireza; Keane, Andrew

doi:10.1109/pesgm.2015.7286457

Cited by 21 publications

(20 citation statements)

References 15 publications

(17 reference statements)

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“…This optimization has one important uncertainty source namely, electricity prices. There are different techniques to handle the uncertainties in decision making frameworks such as information gap decision theory (IGDT) [7], stochastic programming, fuzzy mathematics and robust optimization. These techniques are inherently different in nature and can't be easily compared with each other.…”

Section: A Background and Aimmentioning

confidence: 99%

Optimal DR and ESS Scheduling for Distribution Losses Payments Minimization Under Electricity Price Uncertainty

Soroudi

Siano

Keane

2016

IEEE Trans. Smart Grid

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134

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Section: A Background and Aimmentioning

confidence: 99%

Optimal DR and ESS Scheduling for Distribution Losses Payments Minimization Under Electricity Price Uncertainty

Soroudi

Siano

Keane

2016

IEEE Trans. Smart Grid

Self Cite

134

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“…In 2014, Rabiee et al [92] presented a complete OPF formulation for a power system with uncertain wind power injection through line-commutated converter high-voltage DC (LCC-HVDC) links, voltage source converter (VSC-HVDC), and doubly fed induction generators' (DFIGs). The objective is to maximize the toughness of total costs against the intermittent wind power generation using info-gap decision theory (IGDT).…”

Section: 4mentioning

confidence: 99%

“…In 2014, Panda and Tripathy [104] presented an OPF solution for modified power system in which three conventional generators are replaced by wind-energy conversion systems (WECS). To justify the limitation of reactive power generation capability of WECS, genetic algorithm (GA) and a modified bacteria [92][93][94][95][96][97][98][99][100][101][102][103][104][105][106] foraging algorithm are employed independently, for determining the optimal schedule. In 2015, Panda and Tripathy [105] presented a modified bacteria foraging algorithm, which is capable of handling multiobjective optimization problems.…”

Section: 4mentioning

confidence: 99%

Optimal Power Flow Techniques under Characterization of Conventional and Renewable Energy Sources: A Comprehensive Analysis

Khan

Singh

2017

Journal of Engineering

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The exhaustive knowledge of optimal power flow (OPF) methods is critical for proper system operation and planning, since OPF methods are utilized for finding the optimal state of any system under system constraint conditions, such as loss minimization, reactive power limits, thermal limits of transmission lines, and reactive power optimization. Incorporating renewable energy sources optimized the power flow of system under different constraints. This work presents a comprehensive study of optimal power flows methods with conventional and renewable energy constraints. Additionally, this work presents a progress of optimal power flow solution from its beginning to its present form. Authors classify the optimal power flow methods under different constraints condition of conventional and renewable energy sources. The current and future applications of optimal power flow programs in smart system planning, operations, sensitivity calculation, and control are presented. This study will help the engineers and researchers to optimize power flow with conventional and renewable energy sources.

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“…Therefore, the associated computation of VSC accounts for these uncertainties. Alternative ways to treat this problem are illustrated in [28].…”

Section: General Principlesmentioning

confidence: 99%

Decentralized voltage control of clustered active distribution network by means of energy storage systems

Bahramipanah

Cherkaoui

Paolone

2016

Electric Power Systems Research

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a b s t r a c tThe paper presents a network partitioning strategy for the optimal voltage control of Active Distribution Networks (ADNs) actuated by means of a limited number of Distributed Energy Storage Systems (DESSs). The proposed partitioning uses a linear programming approach by means of the known concept of voltage sensitivities. Then, two decentralized optimal control algorithms are proposed relying, respectively, on the Thévenin equivalents and a recursive approach. These algorithms are developed using the MultiAgent System (MAS) concept. With respect to a centralized control algorithm, the aim of the network clustering is to reduce the number of exchanged messages among the clusters when one of the two proposed decentralized control algorithms is adopted. The effectiveness of the two proposed controls is assessed with respect to the performances of the equivalent centralized control using numerical examples composed by the IEEE 13 and IEEE 123 buses distribution test feeders adapted to include stochastic generation and DESSs.

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Information gap decision theory based OPF with HVDC connected wind farms

Cited by 21 publications

References 15 publications

Optimal DR and ESS Scheduling for Distribution Losses Payments Minimization Under Electricity Price Uncertainty

Optimal DR and ESS Scheduling for Distribution Losses Payments Minimization Under Electricity Price Uncertainty

Optimal Power Flow Techniques under Characterization of Conventional and Renewable Energy Sources: A Comprehensive Analysis

Decentralized voltage control of clustered active distribution network by means of energy storage systems

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