Multi-load level reactive power planning considering slow and fast VAR devices by means of particle swarm optimisation

Eghbal, Mehdi; Yorino, Naoto; El-Araby, E.E.; Zoka, Yoshifumi

doi:10.1049/iet-gtd:20080103

Cited by 44 publications

(48 citation statements)

References 19 publications

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“…This paper presents an approach to optimally allocate multiple FD in deregulated electricity market Series FD Line (6-11), (2-4), (3)(4), (9)(10)(11)(12)(13)(14), and (7-9) (4-5), (9)(10)(11)(12)(13)(14), (13)(14), (1-2), (2-3), (10-11), …”

Section: Resultsmentioning

confidence: 99%

Long‐term economic model for allocation of FACTS devices in restructured power systems integrating wind generation

Elmitwally

Eladl

Morrow

2016

IET Generation, Transmission & Distribution

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Abstract-This paper proposes an approach to optimally allocate multiple types of flexible AC transmission system (FACTS) devices in market-based power systems with wind generation. The main objective is to maximize profit by minimizing device investment cost, and the system's operating cost considering both normal conditions and possible contingencies. The proposed method accurately evaluates the long-term costs and benefits gained by FACTS devices installation to solve a large-scale optimization problem. The objective implies maximizing social welfare as well as minimizing compensations paid for generation rescheduling and load shedding. Many technical operation constraints and uncertainties are included in problem formulation. The overall problem is solved using both Particle Swarm Optimizations (PSO) for attaining optimal FACTS devices allocation as main problem and optimal power flow as sub optimization problem. The effectiveness of the proposed approach is demonstrated on modified IEEE 14-bus test system and IEEE 118-bus test system.

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Section: Resultsmentioning

confidence: 99%

Long‐term economic model for allocation of FACTS devices in restructured power systems integrating wind generation

Elmitwally

Eladl

Morrow

2016

IET Generation, Transmission & Distribution

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“…Subject to: 1) Investment constraints 2) Operation constraints (Load flow constraints) [2] CRF is the Capital Recovery Factor and is calculated as (2) based on the values of interest rate (ir) and life period of VAr devices (Dy).…”

Section: A Var Planning Problemmentioning

confidence: 99%

“…Most of the mentioned works do not consider voltage security constraints, system operation and investment costs in an integrated formulation. An integrated formulation for voltage security VAr planning problem considering the investment costs is presented in the previous work of the authors in [2]. The proposed problem is a large-scale nonlinear mixed integer programming which has been solved by some meta-heuristic techniques.…”

Section: Introductionmentioning

confidence: 99%

“…Then, other slow devices which are cheaper are utilized during preventive control. Details of the experiments on this problem can be found in [2].…”

Section: Introductionmentioning

confidence: 99%

“…To do so, the single objective VAr planning problem introduced in [2], is upgraded and enhanced to a multi objective optimization problem. Total cost of VAr allocation and the amount of ATC are defined as the objective functions in this paper.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Application of Multi‐Objective Evolutionary Optimization Algorithms to Reactive Power Planning Problem

Eghbal

Yorino

Zoka

et al. 2009

IEEJ Transactions Elec Engng

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Abstract-This paper presents a new approach to treat reactive power (VAr) planning problem using multi-objective evolutionary algorithms. Specifically, Strength Pareto Evolutionary Algorithm (SPEA) and Multi-Objective Particle Swarm Optimization (MOPSO) approaches have been developed and successfully applied. The overall problem is formulated as a nonlinear constrained multi-objective optimization problem. Minimizing the total incurred cost and maximizing the amount of Available Transfer Capability (ATC) are defined as the main objective functions. The proposed approaches have been successfully tested on IEEE 14 bus system. As a result a wide set of optimal solutions known as Pareto set is obtained and encouraging results show the superiority of the proposed approaches and confirm their potential to solve such a large scale multi-objective optimization problem.

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Many‐objective Robust Optimization‐based Dynamic VAR Resource Planning

2023

Stability‐Constrained Optimization for Modern Power System Operation and Planning

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Multi-load level reactive power planning considering slow and fast VAR devices by means of particle swarm optimisation

Cited by 44 publications

References 19 publications

Long‐term economic model for allocation of FACTS devices in restructured power systems integrating wind generation

Long‐term economic model for allocation of FACTS devices in restructured power systems integrating wind generation

Application of Multi‐Objective Evolutionary Optimization Algorithms to Reactive Power Planning Problem

Many‐objective Robust Optimization‐based Dynamic VAR Resource Planning

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